Communication method, apparatus, and system

ABSTRACT

A communication method includes sending, by a first data analytics network element, a first request to a service discovery network element. The first request requests information about a second data analytics network element. The first request includes one or more of information about distributed learning or first indication information. The information about distributed learning includes a type of distributed learning. The first indication information indicates a type of the second data analytics network element. The method also includes receiving, by the first data analytics network element, information about the second data analytics network element from the service discovery network element. The second data analytics network element supports the type of distributed learning.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation of International Patent Application No.PCT/CN2021/075317, filed on Feb. 4, 2021, which claims priority toChinese Patent Application No. 202010359339.6, filed on Apr. 29, 2020.The disclosures of the aforementioned applications are herebyincorporated by reference in their entireties.

TECHNICAL FIELD

Embodiments of this application relate to the field of data analytics,and in particular, to a communication method, apparatus, and system.

BACKGROUND

A network data analytics function (NWDAF) network element provides thefollowing functions: data collection (for example, collecting corenetwork data, network management data, service data, and terminal data),data analytics, and data analytics result feedback.

Currently, due to benefit consideration, each domain (for example, aterminal, an access network, a core network, a network managementsystem, and a service provider) is unwilling to open data to anotherdomain. As a result, data is isolated from each other, and a dataanalytics center (for example, the NWDAF network element) cannotcentralize data of each domain and does not support collaborative dataanalytics between domains. Consequently, a data analytics scenario islimited.

SUMMARY

Embodiments of this application provide a communication method,apparatus, and system, so that a data analytics application scenario canbe extended.

According to a first aspect, an embodiment of this application providesa communication method, including: A first data analytics networkelement sends, to a service discovery network element, a first requestthat requests information about a second data analytics network element,where the first request includes one or more of information aboutdistributed learning and first indication information indicates a typeof the second data analytics network element, and the information aboutdistributed learning includes a type of distributed learning requestedby the first data analytics network element. The first data analyticsnetwork element receives information about one or more second dataanalytics network elements from the service discovery network element,where the second data analytics network element supports the type ofdistributed learning requested by the first data analytics networkelement.

This embodiment of this application provides a communication method. Inthe method, the first data analytics network element sends the firstrequest to the service discovery network element, and requests, from theservice discovery network element by using the first request, a featureof the second data analytics network element required by the first dataanalytics network element. In this way, the service discovery networkelement provides, for the first data analytics network element based onthe first request, the information about the one or more second dataanalytics network elements that support the type of distributedlearning. In addition, a type of the second data analytics networkelement is the same as a type of a second data analytics network elementrequested by the first data analytics network element. In this solution,in an aspect, the first data analytics network element can find, byusing the service discovery network element, a data analytics networkelement that can perform distributed learning based training. In anotheraspect, after obtaining the information about the one or more seconddata analytics network elements, the first data analytics networkelement can subsequently collaborate with the one or more second dataanalytics network elements to implement model training when the firstdata analytics network element is to perform model training, so that adata analytics application scenario can be extended.

In a possible implementation, the method provided in this embodiment ofthis application may further include: The first data analytics networkelement determines, based on the information about the one or moresecond data analytics network elements, information about a third dataanalytics network element that performs distributed learning. There isone or more third data analytics network elements. For example, thefirst data analytics network element determines, based on theinformation about the one or more second data analytics networkelements, information about the one or more third data analytics networkelements that perform distributed learning. In this solution, becausethe one or more third data analytics network elements can performdistributed learning based training, in a subsequent distributedlearning based training process, the third data analytics networkelement may not need to provide data for the first data analyticsnetwork element, so that the data may not be transmitted out of a localdomain of the third data analytics network element, and the first dataanalytics network element may still perform model training. On one hand,a data leakage problem is avoided. On the other hand, model training maystill be performed when data exchange cannot be performed between thefirst data analytics network element and the third data analyticsnetwork element. In addition, because data training is performed on eachthird data analytics network element, the distributed training processmay also accelerate an entire model training speed.

In a possible implementation, load of the third data analytics networkelement is lower than a preset load threshold; or a priority of thethird data analytics network element is higher than a preset prioritythreshold, where a range of the third data analytics network elementfalls within a range of the first data analytics network element. Therange of the third data analytics network element includes a public landmobile network PLMN identifier to which the third data analytics networkelement belongs, a range of a network slice instance served by the thirddata analytics network element, a data network name DNN served by thethird data analytics network element, and device vendor information ofthe third data analytics network element.

In a possible implementation, the first request further includes therange of the first data analytics network element, and correspondingly,a range of the second data analytics network element or a range of thethird data analytics network element falls within the range of the firstdata analytics network element. If the first request further includesthe range of the first data analytics network element, the first requestrequests one or more second data analytics network elements that arelocated within the range of the first data analytics network element andthat support the type of distributed learning requested by the firstdata analytics network element.

In a possible implementation, the range of the first data analyticsnetwork element includes one or more of the following information: anarea served by the first data analytics network element, a public landmobile network PLMN identifier to which the first data analytics networkelement belongs, information about a network slice served by the firstdata analytics network element, a data network name DNN served by thefirst data analytics network element, or device vendor information ofthe first data analytics network element.

In a possible implementation, the information about distributed learningfurther includes algorithm information supported by distributedlearning, and correspondingly, the second data analytics network elementor the third data analytics network element supports an algorithmcorresponding to the algorithm information supported by distributedlearning. In this way, the one or more second data analytics networkelements provided by the service discovery network element for the firstdata analytics network element further support the algorithminformation.

In a possible implementation, the algorithm information supported bydistributed learning includes one or more of an algorithm type, analgorithm identifier, and algorithm performance It may be understoodthat algorithm information supported by different second data analyticsnetwork elements may be the same or different.

In a possible implementation, the method provided in this embodiment ofthis application further includes: The first data analytics networkelement receives a sub-model from the one or more third data analyticsnetwork elements, where the sub-model is obtained by the third dataanalytics network element through training based on data obtained by thethird data analytics network element. The first data analytics networkelement determines an updated model based on the sub-model of the one ormore third data analytics network elements. The first data analyticsnetwork element sends the updated model to the one or more third dataanalytics network elements. Because the first data analytics networkelement obtains the updated model based on the sub-model provided bydifferent data analytics network element in the one or more third dataanalytics network elements, each third data analytics network elementmay not need to provide data used for training for the first dataanalytics network element, to avoid data leakage.

In a possible implementation, the method provided in this embodiment ofthis application further includes: The first data analytics networkelement determines a target model based on the updated model. The firstdata analytics network element sends, to the one or more second dataanalytics network elements, the target model and one or more of thefollowing information corresponding to the target model: a modelidentifier, a model version identifier, or a data analytics identifier.In this way, each second data analytics network element may obtain thetarget model determined by the first data analytics network element. Forexample, the target model may be a service experience model.

In a possible implementation, before that the first data analyticsnetwork element receives a sub-model from the one or more third dataanalytics network elements, the method provided in this embodiment ofthis application further includes: The first data analytics networkelement sends a configuration parameter to the one or more third dataanalytics network elements, where the configuration parameter is aparameter used by the third data analytics network element to train thesub-model. In this way, the third data analytics network elementconfigures a related parameter in the distributed learning basedtraining process based on the configuration parameter.

In a possible implementation, the configuration parameter includes oneor more of the following information: an initial model, a training setselection criterion, a feature generation method, a training terminationcondition, maximum training time, or maximum waiting time.

In a possible implementation, the type of distributed learning includesone of horizontal learning, vertical learning, and transfer learning.

In a possible implementation, the type of the second data analyticsnetwork element is one of the following: a client, a local trainer, or apartial trainer.

In a possible implementation, the method provided in this embodiment ofthis application further includes: The first data analytics networkelement sends, to the service discovery network element, a secondrequest that requests to register information about the first dataanalytics network element, where the information about the first dataanalytics network element includes one or more of the followinginformation corresponding to the first data analytics network element:the information about distributed learning or second indicationinformation, and the second indication information indicates a type ofthe first data analytics network element. In this way, the informationabout the first data analytics network element is registered, so thatanother device subsequently determines the first data analytics networkelement by using the service discovery network element.

In a possible implementation, the information about the first dataanalytics network element further includes one or more of the range ofthe first data analytics network element, an identifier of the firstdata analytics network element, and address information of the firstdata analytics network element.

In a possible implementation, the type of the first data analyticsnetwork element includes one of the following information: a server, acoordinator, a centralized trainer, and a global trainer.

In a possible implementation, distributed learning is federatedlearning.

In a possible implementation, the second data analytics network elementis a terminal.

According to a second aspect, an embodiment of this application providesa communication method. The method includes: A service discovery networkelement receives, from a first data analytics network element, a firstrequest that requests information about a second data analytics networkelement, where the first request includes one or more of the followinginformation: information about distributed learning and first indicationinformation, the information about distributed learning includes a typeof distributed learning requested by the first data analytics networkelement, and the first indication information indicates a type of thesecond data analytics network element. The service discovery networkelement determines, based on the first request, information about one ormore second data analytics network elements that support the type ofdistributed learning. The service discovery network element sends theinformation about the one or more second data analytics network elementsto the first data analytics network element.

In a possible implementation, the first request in the method providedin this embodiment of this application further includes a range of thefirst data analytics network element, and correspondingly, a range ofthe second data analytics network element falls within the range of thefirst data analytics network element. For example, that the servicediscovery network element determines, based on the first request,information about one or more second data analytics network elementsthat support the type of distributed learning includes: The servicediscovery network element determines, as the one or more second dataanalytics network elements, one or more data analytics network elementsthat are located within the range of the first data analytics networkelement and that support the type of distributed learning.

In a possible implementation, the information about distributed learningfurther includes algorithm information supported by distributedlearning, and correspondingly, the second data analytics network elementsupports an algorithm corresponding to the algorithm informationsupported by distributed learning. For example, that a service discoverynetwork element determines, based on the first request, informationabout one or more second data analytics network elements that supportthe type of distributed learning includes: The service discovery networkelement determines, as the one or more second data analytics networkelements, one or more data analytics network elements that support thetype of distributed learning and the algorithm information supported bydistributed learning.

In a possible implementation, the method provided in this embodiment ofthis application further includes: The service discovery network elementreceives, from the first data analytics network element, a secondrequest that requests to register information about the first dataanalytics network element, where the information about the first dataanalytics network element includes one or more of the followinginformation corresponding to the first data analytics network element:the information about distributed learning or second indicationinformation, and the second indication information indicates a type ofthe first data analytics network element. The service discovery networkelement registers the information about the first data analytics networkelement based on the second request.

In a possible implementation, the information about the first dataanalytics network element further includes one or more of the range ofthe first data analytics network element, an identifier of the firstdata analytics network element, and address information of the firstdata analytics network element.

In a possible implementation, that the service discovery network elementregisters the information about the first data analytics network elementbased on the second request includes: The service discovery networkelement stores the information about the first data analytics networkelement in the service discovery network element, or the servicediscovery network element stores the information about the first dataanalytics network element in a user data management network element.

In a possible implementation, the method provided in this embodiment ofthis application further includes: The service discovery network elementreceives, from the one or more second data analytics network elements, athird request that requests to register the information about the seconddata analytics network element, where the information about the seconddata analytics network element includes one or more of the followinginformation corresponding to the second data analytics network element:the information about distributed learning and third indicationinformation, and the third indication information indicates the type ofthe second data analytics network element. The service discovery networkelement registers the information about the one or more second dataanalytics network elements based on the third request.

In a possible implementation, the information about the second dataanalytics network element further includes one or more of the range ofthe second data analytics network element, an identifier of the seconddata analytics network element, and address information of the seconddata analytics network element.

In a possible implementation, that the service discovery network elementregisters the information about the one or more second data analyticsnetwork elements based on the third request includes: The servicediscovery network element stores the information about the one or moresecond data analytics network elements in the service discovery networkelement.

In a possible implementation, that the service discovery network elementregisters the information about the one or more second data analyticsnetwork elements based on the third request includes: The servicediscovery network element stores the information about the one or moresecond data analytics network elements in the user data managementnetwork element.

In a possible implementation, the type of the first data analyticsnetwork element includes one of the following information: a server, acoordinator, a centralized trainer, and a global trainer.

In a possible implementation, the type of the second data analyticsnetwork element includes one of the following information: a client, alocal trainer, or a partial trainer.

In a possible implementation, distributed learning is federatedlearning.

In a possible implementation, the second data analytics network elementis a terminal.

According to a third aspect, an embodiment of this application providesa communication method. The method includes: A third data analyticsnetwork element determines a sub-model, where the sub-model is obtainedby the third data analytics network element through training based ondata obtained by the third data analytics network element; and the thirddata analytics network element sends the sub-model to the first dataanalytics network element.

In a possible implementation, the method provided in this embodiment ofthis application may further include: The sub-model is obtained by thethird data analytics network element through training based on dataobtained by the third data analytics network element from a range of thethird data analytics network element.

In a possible implementation, the method provided in this embodiment ofthis application may further include: The third data analytics networkelement receives an updated model from the first data analytics networkelement, where the updated model is obtained by using sub-modelsprovided by a plurality of different third data analytics networkelements.

In a possible implementation, the method provided in this embodiment ofthis application may further include: The third data analytics networkelement receives a target model from the first data analytics networkelement.

In a possible implementation, the method provided in this embodiment ofthis application may further include: The third data analytics networkelement receives a configuration parameter from the first data analyticsnetwork element, where the configuration parameter is a parameter usedby the third data analytics network element to train the sub-model.

In a possible implementation, the configuration parameter includes oneor more of the following information: an initial model, a training setselection criterion, a feature generation method, a training terminationcondition, maximum training time, or maximum waiting time.

In a possible implementation, a type of distributed learning includesone of horizontal learning, vertical learning, and transfer learning.

In a possible implementation, a type of the third data analytics networkelement is one of the following: a client, a local trainer, or a partialtrainer.

In a possible implementation, the range of the third data analyticsnetwork element falls within a range of the first data analytics networkelement.

In a possible implementation, the method provided in this embodiment ofthis application may further include: The third data analytics networkelement sends, to a service discovery network element, a third requestthat requests to register information about the third data analyticsnetwork element, where the information about the third data analyticsnetwork element includes one or more of the following informationcorresponding to the third data analytics network element: informationabout distributed learning or third indication information, and thethird indication information indicates the type of the third dataanalytics network element. The information about distributed learningcorresponding to the third data analytics network element includes atype of distributed learning supported by the third data analyticsnetwork element and/or algorithm information supported by distributedlearning supported by the third data analytics network element.

In a possible implementation, the information about the third dataanalytics network element further includes one or more of the range ofthe third data analytics network element, an identifier of the thirddata analytics network element, and address information of the thirddata analytics network element.

In a possible implementation, a type of the first data analytics networkelement includes one of the following information: a server, acoordinator, a centralized trainer, and a global trainer.

In a possible implementation, distributed learning is federatedlearning.

According to a fourth aspect, an embodiment of this application providesa communication apparatus. The communication apparatus may implement thecommunication method according to any one of the first aspect or thepossible implementations of the first aspect, and therefore may furtherimplement beneficial effects according to any one of the first aspect orthe possible implementations of the first aspect. The communicationapparatus may be a first data analytics network element, or may be anapparatus that may support the first data analytics network element inimplementing any one of the first aspect or the possible implementationsof the first aspect, for example, a chip used in the first dataanalytics network element. The communication apparatus may implement theforegoing methods by using software or hardware, or by executingcorresponding software by hardware.

In an example, this embodiment of this application provides acommunication apparatus, including a communication unit and a processingunit, where the communication unit is configured to receive and sendinformation, and the processing unit is configured to processinformation. For example, the communication unit is configured to send,to a service discovery network element, a first request that requestsinformation about a second data analytics network element, where thefirst request includes one or more of information about distributedlearning and first indication information indicates a type of the seconddata analytics network element, and the information about distributedlearning includes a type of distributed learning requested by the firstdata analytics network element. The communication unit is furtherconfigured to receive information about one or more second dataanalytics network elements from the service discovery network element,where the second data analytics network element supports the type ofdistributed learning requested by the first data analytics networkelement.

In a possible implementation, the processing unit is configured todetermine, based on the information about the one or more second dataanalytics network elements, information about a third data analyticsnetwork element that performs distributed learning, where there is oneor more third data analytics network elements.

In a possible implementation, load of the third data analytics networkelement is lower than a preset load threshold; or a priority of thethird data analytics network element is higher than a preset prioritythreshold, where a range of the third data analytics network elementfalls within a range of the first data analytics network element.

In a possible implementation, the first request further includes therange of the first data analytics network element, and correspondingly,a range of the second data analytics network element or a range of thethird data analytics network element falls within the range of the firstdata analytics network element. It may be understood that, if the firstrequest further includes the range of the first data analytics networkelement, the first request requests one or more second data analyticsnetwork elements that are located within the range of the first dataanalytics network element and that support the type of distributedlearning requested by the first data analytics network element.

In a possible implementation, the range of the first data analyticsnetwork element includes one or more of the following information: anarea served by the first data analytics network element, a public landmobile network PLMN identifier to which the first data analytics networkelement belongs, information about a network slice served by the firstdata analytics network element, a data network name DNN served by thefirst data analytics network element, or device vendor information ofthe first data analytics network element.

In a possible implementation, the information about distributed learningfurther includes algorithm information supported by distributedlearning, and correspondingly, the second data analytics network elementor the third data analytics network element supports an algorithmcorresponding to the algorithm information supported by distributedlearning. In this way, the one or more second data analytics networkelements provided by the service discovery network element for the firstdata analytics network element further support the algorithm informationsupported by distributed learning.

In a possible implementation, the algorithm information supported bydistributed learning includes one or more of an algorithm type, analgorithm identifier, and algorithm performance It may be understoodthat algorithm information supported by different second data analyticsnetwork elements or third data analytics network elements may be thesame or different.

In a possible implementation, the communication unit is furtherconfigured to receive a sub-model from the one or more third dataanalytics network elements, where the sub-model is obtained by the thirddata analytics network element through training based on data obtainedby the third data analytics network element. The processing unit isconfigured to determine an updated model based on the sub-model of theone or more third data analytics network elements. The communicationunit is further configured to send the updated model to the one or morethird data analytics network elements.

In a possible implementation, the processing unit is further configuredto determine a target model based on the updated model. Thecommunication unit is further configured to send, to the one or moresecond data analytics network elements, the target model and one or moreof the following information corresponding to the target model: a modelidentifier, a model version identifier, or a data analytics identifier.Although not all of the one or more second data analytics networkelements participate in a training process of the target model, eachsecond data analytics network element may obtain, by sending the targetmodel to the one or more second data analytics network elements, thetarget model determined by the first data analytics network element. Forexample, the target model may be a service experience model.

In a possible implementation, the communication unit is furtherconfigured to send a configuration parameter to the one or more thirddata analytics network elements, where the configuration parameter is aparameter used by the third data analytics network element to train thesub-model. In this way, the third data analytics network elementconfigures a related parameter in a distributed learning based trainingprocess based on the configuration parameter.

In a possible implementation, the configuration parameter includes oneor more of the following information: an initial model, a training setselection criterion, a feature generation method, a training terminationcondition, maximum training time, or maximum waiting time.

In a possible implementation, the type of distributed learning includesone of horizontal learning, vertical learning, and transfer learning.

In a possible implementation, the type of the second data analyticsnetwork element is one of the following: a client, a local trainer, or apartial trainer.

In a possible implementation, the communication unit is furtherconfigured to send, to the service discovery network element, a secondrequest that requests to register information about the first dataanalytics network element. The information about the first dataanalytics network element includes one or more of the followinginformation corresponding to the first data analytics network element:the information about distributed learning and second indicationinformation, and the second indication information indicates a type ofthe first data analytics network element. In this way, the informationabout the first data analytics network element is registered, so thatanother device subsequently determines the first data analytics networkelement by using the service discovery network element.

In a possible implementation, the information about the first dataanalytics network element further includes one or more of the range ofthe first data analytics network element, an identifier of the firstdata analytics network element, and address information of the firstdata analytics network element.

In a possible implementation, the type of the first data analyticsnetwork element includes one of the following information: a server, acoordinator, a centralized trainer, and a global trainer.

In a possible implementation, distributed learning is federatedlearning.

In a possible implementation, the second data analytics network elementis a terminal.

In another example, this embodiment of this application provides acommunication apparatus. The communication apparatus may be the firstdata analytics network element, or may be the apparatus (for example, achip) used in the first data analytics network element. Thecommunication apparatus may include the processing unit and thecommunication unit. The communication apparatus may further include astorage unit. The storage unit is configured to store computer programcode. The computer program code includes instructions. The processingunit executes the instructions stored in the storage unit, to enable thecommunication apparatus to implement the method according to any one ofthe first aspect or the possible implementations of the first aspect.When the communication apparatus is the first data analytics networkelement, the processing unit may be a processor. The communication unitmay be a communication interface. The storage unit may be a memory. Whenthe communication apparatus is the chip in the first data analyticsnetwork element, the processing unit may be the processor, and thecommunication unit may be collectively referred to as the communicationinterface. For example, the communication interface may be aninput/output interface, a pin, or a circuit. The processing unitexecutes the computer program code stored in the storage unit, to enablethe first data analytics network element to implement the methodaccording to any one of the first aspect or the possible implementationsof the first aspect. The storage unit may be a storage unit (forexample, a register or a cache) in the chip, or may be a storage unit(for example, a read-only memory or a random access memory) that is inthe first data analytics network element and that is outside the chip.

In a possible implementation, the processor, the communicationinterface, and the memory are coupled to each other.

According to a fifth aspect, an embodiment of this application providesa communication apparatus. The communication apparatus may implement thecommunication method according to any one of the second aspect or thepossible implementations of the second aspect, and therefore may furtherimplement beneficial effects according to any one of the second aspector the possible implementations of the second aspect. The communicationapparatus may be a service discovery network element, or may be anapparatus that may support the service discovery network element inimplementing any one of the second aspect or the possibleimplementations of the second aspect, for example, a chip used in aservice discovery network element. The communication apparatus mayimplement the foregoing methods by using software or hardware, or byexecuting corresponding software by hardware.

In an example, this embodiment of this application provides acommunication apparatus, including: a communication unit, configured toreceive, from a first data analytics network element, a first requestthat requests information about a second data analytics network element,where the first request includes one or more of the followinginformation: information about distributed learning and first indicationinformation, the information about distributed learning includes a typeof distributed learning requested by the first data analytics networkelement, and the first indication information indicates a type of thesecond data analytics network element; and a processing unit, configuredto determine, based on the first request, information about one or moresecond data analytics network elements that support the type ofdistributed learning. The communication unit is further configured tosend the information about the one or more second data analytics networkelements to the first data analytics network element.

In a possible implementation, the first request in the method providedin this embodiment of this application further includes a range of thefirst data analytics network element, and correspondingly, a range ofthe second data analytics network element falls within the range of thefirst data analytics network element. For example, that a servicediscovery network element determines, based on the first request,information about one or more second data analytics network elementsthat support the type of distributed learning includes: The servicediscovery network element determines, as the one or more second dataanalytics network elements, one or more data analytics network elementsthat are located within the range of the first data analytics networkelement and that support the type of distributed learning.

In a possible implementation, the information about distributed learningfurther includes algorithm information supported by distributedlearning, and correspondingly, the second data analytics network elementsupports an algorithm corresponding to the algorithm informationsupported by distributed learning. For example, that the processing unitis configured to determine, based on the first request, informationabout one or more second data analytics network elements that supportthe type of distributed learning includes: The processing unit isconfigured to determine, as the one or more second data analyticsnetwork elements, one or more data analytics network elements thatsupport the type of distributed learning and the algorithm informationsupported by distributed learning.

In a possible implementation, the communication unit is furtherconfigured to receive, from the first data analytics network element, asecond request that requests to register information about the firstdata analytics network element, where the information about the firstdata analytics network element includes one or more of the followinginformation corresponding to the first data analytics network element:information about distributed learning and second indicationinformation. The second indication information indicates a type of thefirst data analytics network element. The processing unit is configuredto register the information about the first data analytics networkelement based on the second request. The information about distributedlearning corresponding to the first data analytics network elementincludes a type of distributed learning supported by the first dataanalytics network element and/or algorithm information supported bydistributed learning supported by the first data analytics networkelement.

In a possible implementation, the information about the first dataanalytics network element further includes one or more of the range ofthe first data analytics network element, an identifier of the firstdata analytics network element, and address information of the firstdata analytics network element.

In a possible implementation, that the processing unit is configured toregister the information about the first data analytics network elementbased on the second request includes: The processing unit is configuredto store the information about the first data analytics network elementin the service discovery network element, or the processing unit isconfigured to store the information about the first data analyticsnetwork element in a user data management network element.

In a possible implementation, the communication unit is furtherconfigured to receive, from the one or more second data analyticsnetwork elements, a third request that requests to register theinformation about the second data analytics network element, where theinformation about the second data analytics network element includes oneor more of the following information corresponding to the second dataanalytics network element: the information about distributed learning orthird indication information, and the third indication informationindicates a type of the second data analytics network element. Theprocessing unit is configured to register the information about the oneor more second data analytics network elements based on the thirdrequest. The information about distributed learning corresponding to thesecond data analytics network element includes the type of distributedlearning supported by the second data analytics network element and/orthe algorithm information supported by distributed learning supported bythe second data analytics network element.

In a possible implementation, the information about the second dataanalytics network element further includes one or more of a range of thesecond data analytics network element, an identifier of the second dataanalytics network element, and address information of the second dataanalytics network element.

In a possible implementation, that the processing unit is configured toregister the information about the one or more second data analyticsnetwork elements based on the third request includes: The processingunit is configured to store the information about the one or more seconddata analytics network elements in the service discovery networkelement, or the processing unit is configured to store the informationabout the one or more second data analytics network elements in the userdata management network element.

In a possible implementation, the type of the first data analyticsnetwork element includes one or more of the following information: aserver, a coordinator, a centralized trainer, and a global trainer.

In a possible implementation, the type of the second data analyticsnetwork element is one of the following: a client, a local trainer, or apartial trainer.

In a possible implementation, distributed learning includes federatedlearning.

In a possible implementation, the second data analytics network elementis a terminal.

In another example, this embodiment of this application provides acommunication apparatus. The communication apparatus may be the servicediscovery network element, or may be the chip in the service discoverynetwork element. The communication apparatus may include the processingunit and the communication unit. The communication apparatus may furtherinclude a storage unit. The storage unit is configured to store computerprogram code. The computer program code includes instructions. Theprocessing unit executes the instructions stored in the storage unit, toenable the communication apparatus to implement the method according toany one of the second aspect or the possible implementations of thesecond aspect. When the communication apparatus is the service discoverynetwork element, the processing unit may be a processor. Thecommunication unit may be a communication interface. The storage unitmay be a memory. When the communication apparatus is the chip in theservice discovery network element, the processing unit may be theprocessor, and the communication unit may be collectively referred to asthe communication interface. For example, the communication interfacemay be an input/output interface, a pin, or a circuit. The processingunit executes the computer program code stored in the storage unit, toenable the service discovery network element to implement the methodaccording to any one of the second aspect or the possibleimplementations of the second aspect. The storage unit may be a storageunit (for example, a register or a cache) in the chip, or may be astorage unit (for example, a read-only memory or a random access memory)that is in the service discovery network element and that is outside thechip.

In a possible implementation, the processor, the communicationinterface, and the memory are coupled to each other.

According to a sixth aspect, an embodiment of this application providesa communication apparatus. The communication apparatus may implement thecommunication method according to any one of the third aspect or thepossible implementations of the third aspect, and therefore may alsoimplement beneficial effects according to any one of the third aspect orthe possible implementations of the third aspect. The communicationapparatus may be a third data analytics network element, or may be anapparatus that may support the third data analytics network element inimplementing any one of the third aspect or the possible implementationsof the third aspect, for example, a chip used in the third dataanalytics network element. The communication apparatus may implement theforegoing methods by using software or hardware, or by executingcorresponding software by hardware.

In an example, this embodiment of this application provides acommunication apparatus. The apparatus includes a processing unit,configured to determine a sub-model, where the sub-model is obtained bythe processing unit through training based on data obtained by acommunication unit; and the communication unit, configured to send thesub-model to a first data analytics network element.

In a possible implementation, the communication unit is furtherconfigured to receive an updated model from the first data analyticsnetwork element, where the updated model is obtained by using sub-modelsprovided by a plurality of different third data analytics networkelements.

In a possible implementation, the communication unit is furtherconfigured to receive a target model from the first data analyticsnetwork element.

In a possible implementation, the communication unit is furtherconfigured to receive a configuration parameter from the first dataanalytics network element, where the configuration parameter is aparameter used by the third data analytics network element to train thesub-model.

In a possible implementation, the configuration parameter includes oneor more of the following information: an initial model, a training setselection criterion, a feature generation method, a training terminationcondition, maximum training time, or maximum waiting time.

In a possible implementation, a type of distributed learning includesone of horizontal learning, vertical learning, and transfer learning.

In a possible implementation, the communication unit is furtherconfigured to send, to a service discovery network element, a thirdrequest that requests to register information about the third dataanalytics network element, where the information about the third dataanalytics network element includes one or more of the followinginformation corresponding to the third data analytics network element:information about distributed learning or third indication information,and the third indication information indicates a type of the third dataanalytics network element. The information about distributed learningcorresponding to the third data analytics network element includes atype of distributed learning supported by the third data analyticsnetwork element and/or algorithm information supported by distributedlearning supported by the third data analytics network element.

In a possible implementation, the information about the third dataanalytics network element further includes one or more of a range of thethird data analytics network element, an identifier of the third dataanalytics network element, and address information of the third dataanalytics network element.

In a possible implementation, a type of the first data analytics networkelement includes one of the following information: a server, acoordinator, a centralized trainer, and a global trainer.

In a possible implementation, distributed learning is federatedlearning.

In a possible implementation, the type of the third data analyticsnetwork element is one of the following information: a client, a localtrainer, or a partial trainer.

In a possible implementation, the range of the third data analyticsnetwork element falls within a range of the first data analytics networkelement.

In another example, this embodiment of this application provides acommunication apparatus. The communication apparatus may be the thirddata analytics network element, or may be the chip in the third dataanalytics network element. The communication apparatus may include theprocessing unit and the communication unit. The communication apparatusmay further include a storage unit. The storage unit is configured tostore computer program code. The computer program code includesinstructions. The processing unit executes the instructions stored inthe storage unit, to enable the communication apparatus to implement themethod according to any one of the third aspect or the possibleimplementations of the third aspect. When the communication apparatus isthe third data analytics network element, the processing unit may be aprocessor. The communication unit may be a communication interface. Thestorage unit may be a memory. When the communication apparatus is thechip in the third data analytics network element, the processing unitmay be the processor, and the communication unit may be collectivelyreferred to as the communication interface. For example, thecommunication interface may be an input/output interface, a pin, or acircuit. The processing unit executes the computer program code storedin the storage unit, to enable the third data analytics network elementto implement the method according to any one of the third aspect or thepossible implementations of the third aspect. The storage unit may be astorage unit (for example, a register or a cache) in the chip, or may bea storage unit (for example, a read-only memory or a random accessmemory) that is in the third data analytics network element and that isoutside the chip.

In a possible implementation, the processor, the communicationinterface, and the memory are coupled to each other.

According to a seventh aspect, an embodiment of this applicationprovides a computer program product including instructions. When theinstructions are run on a computer, the computer is enabled to performthe communication method according to any one of the first aspect or thepossible implementations of the first aspect.

According to an eighth aspect, an embodiment of this applicationprovides a computer program product including instructions. When theinstructions are run on a computer, the computer is enabled to performthe communication method according to any one of the second aspect orthe possible implementations of the second aspect.

According to a ninth aspect, an embodiment of this application providesa computer program product including instructions. When the instructionsare run on a computer, the computer is enabled to perform thecommunication method according to any one of the third aspect or thepossible implementations of the third aspect.

According to a tenth aspect, an embodiment of this application providesa computer-readable storage medium. The computer readable storage mediumstores a computer program or instructions. When the computer program orthe instructions are run on a computer, the computer is enabled toperform the communication method according to any one of the firstaspect or the possible implementations of the first aspect.

According to an eleventh aspect, an embodiment of this applicationprovides a computer-readable storage medium. The computer readablestorage medium stores a computer program or instructions. When thecomputer program or the instructions are run on a computer, the computeris enabled to perform the communication method according to any one ofthe second aspect or the possible implementations of the second aspect.

According to a twelfth aspect, an embodiment of this applicationprovides a computer-readable storage medium. The computer readablestorage medium stores a computer program or instructions. When thecomputer program or the instructions are run on a computer, the computeris enabled to perform the communication method according to any one ofthe third aspect or the possible implementations of the third aspect.

According to a thirteenth aspect, an embodiment of this applicationprovides a communication apparatus. The communication apparatus includesat least one processor, and the at least one processor is configured torun a computer program or instructions stored in a memory, to implementthe communication method according to any one of the first aspect or thepossible implementations of the first aspect.

According to a fourteenth aspect, an embodiment of this applicationprovides a communication apparatus. The communication apparatus includesat least one processor, and the at least one processor is configured torun a computer program or instructions stored in a memory, to implementthe communication method according to any one of the second aspect orthe possible implementations of the second aspect.

According to a fifteenth aspect, an embodiment of this applicationprovides a communication apparatus. The communication apparatus includesat least one processor, and the at least one processor is configured torun a computer program or instructions stored in a memory, to implementthe communication method according to any one of the third aspect or thepossible implementations of the third aspect.

In a possible implementation, the communication apparatuses described inthe thirteenth aspect to a fifteenth aspect may further include amemory.

According to a sixteenth aspect, an embodiment of this applicationprovides a communication apparatus. The communication apparatus includesa processor and a storage medium. The storage medium storesinstructions. When the instructions are run by the processor, thecommunication method according to any one of the first aspect or thepossible implementations of the first aspect is implemented.

According to a seventeenth aspect, an embodiment of this applicationprovides a communication apparatus. The communication apparatus includesa processor and a storage medium. The storage medium storesinstructions. When the instructions are run by the processor, thecommunication method according to any one of the second aspect or thepossible implementations of the second aspect is implemented.

According to an eighteenth aspect, an embodiment of this applicationprovides a communication apparatus. The communication apparatus includesa processor and a storage medium. The storage medium storesinstructions. When the instructions are run by the processor, thecommunication method according to any one of the third aspect or thepossible implementations of the third aspect is implemented.

According to a nineteenth aspect, an embodiment of this applicationprovides a communication apparatus. The communication apparatus includesone or more modules, configured to implement the methods in the firstaspect, the second aspect, and the third aspect. The one or more modulesmay correspond to the steps in the methods in the first aspect, thesecond aspect, and the third aspect.

According to a twentieth aspect, an embodiment of this applicationprovides a chip. The chip includes a processor and a communicationinterface. The communication interface is coupled to the processor, andthe processor is configured to run a computer program or instructions,to implement the communication method according to any one of the firstaspect or the possible implementations of the first aspect. Thecommunication interface is configured to communicate with a module otherthan the chip.

According to a twenty-first aspect, an embodiment of this applicationprovides a chip. The chip includes a processor and a communicationinterface. The communication interface is coupled to the processor, andthe processor is configured to run a computer program or instructions,to implement the communication method according to any one of the secondaspect or the possible implementations of the second aspect. Thecommunication interface is configured to communicate with a module otherthan the chip.

According to a twenty-second aspect, an embodiment of this applicationprovides a chip. The chip includes a processor and a communicationinterface. The communication interface is coupled to the processor, andthe processor is configured to run a computer program or instructions,to implement the method according to any one of the third aspect or thepossible implementations of the third aspect. The communicationinterface is configured to communicate with a module other than thechip.

The chip provided in this embodiment of this application furtherincludes a memory, configured to store the computer program or theinstructions.

According to a twenty-third aspect, an embodiment of this applicationprovides an apparatus, configured to perform the communication methodaccording to any one of the first aspect or the possible implementationsof the first aspect.

According to a twenty-fourth aspect, an embodiment of this applicationprovides an apparatus, configured to perform the communication methodaccording to any one of the second aspect or the possibleimplementations of the second aspect.

According to a twenty-fifth aspect, an embodiment of this applicationprovides an apparatus, configured to perform the communication methodaccording to any one of the third aspect or the possible implementationsof the third aspect.

Any apparatus, computer storage medium, computer program product, chip,or communication system provided above is configured to perform thecorresponding method provided above. Therefore, for beneficial effectsthat can be achieved by the apparatus, computer storage medium, computerprogram product, chip, or communication system provided above, refer tothe beneficial effects of the corresponding solution in thecorresponding method provided above. Details are not described hereinagain.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram of an architecture of a communication systemaccording to an embodiment of this application;

FIG. 2 is a diagram of a 5G network architecture according to anembodiment of this application;

FIG. 3 is a diagram of an architecture of federated learning accordingto an embodiment of this application;

FIG. 4 is a schematic diagram of a scenario according to an embodimentof this application;

FIG. 5 is a schematic diagram of another scenario according to anembodiment of this application;

FIG. 6 is a schematic flowchart of a communication method according toan embodiment of this application;

FIG. 7A and FIG. 7B each are a schematic flowchart of anothercommunication method according to an embodiment of this application;

FIG. 8 is a detailed embodiment of a communication method according toan embodiment of this application;

FIG. 9A and FIG. 9B each are a detailed embodiment of anothercommunication method according to an embodiment of this application;

FIG. 10 is a schematic diagram of an architecture of model trainingaccording to an embodiment of this application;

FIG. 11 is another schematic diagram of an architecture of modeltraining according to an embodiment of this application;

FIG. 12 is a schematic diagram of a structure of a communicationapparatus according to an embodiment of this application;

FIG. 13 is a schematic diagram of a structure of another communicationapparatus according to an embodiment of this application;

FIG. 14 is a schematic diagram of a structure of a communication deviceaccording to an embodiment of this application; and

FIG. 15 is a schematic diagram of a structure of a chip according to anembodiment of this application.

DESCRIPTION OF EMBODIMENTS

To clearly describe technical solutions in embodiments of thisapplication, terms such as “first” and “second” are used in embodimentsof this application to distinguish between same items or similar itemsthat provide basically same functions or purposes. For example, firstindication information and the second indication information are onlyused to distinguish between different indication information, and do notlimit sequences of the first indication information and the secondindication information. A person skilled in the art may understand thatthe terms such as “first” and “second” do not limit a quantity or anexecution sequence, and the terms such as “first” and “second” do notindicate a definite difference. For example, a first data analyticsnetwork element may include one or more data analytics network elements,and a second data analytics network element may also include one or moredata analytics network elements.

It should be noted that, in this application, the word “example”, “forexample”, or the like is used to represent giving an example, anillustration, or a description. Any embodiment or design schemedescribed as an “example” or “for example” in this application shouldnot be considered as being more preferred or having more advantages thananother embodiment or design scheme. Exactly, using of the word“exemplary” or “example” or the like is intended to present a relativeconcept in a specific manner.

In this application, “at least one” means one or more, and “a pluralityof” means two or more. “And/or” describes an association relationshipbetween associated objects, and indicates that three relationships mayexist. For example, A and/or B may indicate the following cases: Only Aexists, both A and B exist, and only B exists, where A and B may besingular or plural. The character “I” generally indicates an “or”relationship between the associated objects. “At least one of thefollowing items (pieces)” or a similar expression thereof indicates anycombination of these items, including a single item (piece) or anycombination of a plurality of items (pieces). For example, at least oneitem (piece) of a, b, or c may indicate: a, b, c, a and b, a and c, band c, or a, b, and c, where a, b, and c may be singular or plural.

The technical solutions in embodiments of this application may beapplied to various communication systems, for example, code divisionmultiple access (CDMA), time division multiple access (TDMA), frequencydivision multiple access (FDMA), orthogonal frequency division multipleaccess (OFDMA), single carrier frequency division multiple access(SC-FDMA), and another system. Terms “system” and “network” may beinterchanged with each other. Long term evolution (LTE) and variousversions evolved based on LTE in 3GPP are a new version of a UMTS thatuses an E-UTRA. A 5G communication system and a new radio (NR) arenext-generation communication systems under research. In addition, thecommunication system may be further applied to a future-orientedcommunication technology, and are all applicable to the technicalsolutions provided in embodiments of this application.

FIG. 1 shows an architecture of a communication system according to anembodiment of this application. The communication system includes a dataanalytics network element 100, one or more data analytics networkelements (for example, a data analytics network element 201 to a dataanalytics network element 20 n) that communicate with the data analyticsnetwork element 100, and a service discovery network element 300, wheren is an integer greater than or equal to 1.

The data analytics network element 100 and the one or more dataanalytics network elements (for example, the data analytics networkelement 201 to the data analytics network element 20 n) each have adistributed learning capability.

For example, a type of the data analytics network element 100 or a roleplayed by the data analytics network element 100 in distributed learningmay be one or more of the following information: a server, acoordinator, a centralized trainer, and a global trainer. A type of anydata analytics network element from the data analytics network element201 to the data analytics network element 20 n or a role played by anydata analytics network element in distributed learning may be one ormore of the following: a client, a local trainer, a distributed trainer,or a partial trainer. A deployment mode shown in FIG. 1 may be referredto as a server-client mode.

The type of the data analytics network element in this embodiment ofthis application may be the role played by the data analytics networkelement in distributed learning. For example, if the type of the dataanalytics network element 100 is a server, it indicates that the roleplayed by the data analytics network element 100 in distributed learningis a server type.

In this embodiment of this application, the data analytics networkelement 100 may be considered as a (central) server node, and the dataanalytics network element 201 to the data analytics network element 20 nmay be considered as (edge) client (client) nodes.

Each of the data analytics network element 201 to the data analyticsnetwork element 20 n has a respective range, and some or all of the dataanalytics network element 201 to the data analytics network element 20 nare located within a range of the data analytics network element 100.

In this embodiment of this application, any data analytics networkelement may be independently deployed, or may be co-deployed with anetwork function network element (for example, a session managementfunction (SMF) network element, an access and mobility managementfunction (AMF) network element, or a policy control function (PCF)network element) in a 5G network. For example, the data analyticsnetwork element may be deployed on an existing 5GC NF based on a networkelement data volume or a function requirement. For example, a dataanalytics network element having a terminal mobility (UE Mobility or UEMoving Trajectory) analytics capability is co-deployed with the AMFnetwork element. In this way, terminal location information on the AMFnetwork element is not leaked from a core network, to avoid user dataprivacy and data security issues. In addition, for internal networkelement data analytics, each 5GC NF may alternatively have a built-inintelligent module (for example, a built-in NWDAF functional module),and implement a self-closed loop based on data of the 5GC NE The dataanalytics network element performs closed-loop control based on a dataflow, only for a cross-network-element data closed loop. This is notlimited in this embodiment of this application.

To avoid data leakage, original data obtained by each of the dataanalytics network element 201 to the data analytics network element 20 nis distributed on each of the data analytics network element 201 to thedata analytics network element 20 n. The data analytics network element100 may not have original data, or the data analytics network element100 cannot collect and obtain the original data that is obtained by eachof the data analytics network element 201 to the data analytics networkelement 20 n and that is distributed on each of the data analyticsnetwork element 201 to the data analytics network element 20 n, and eachof the data analytics network element 201 to the data analytics networkelement 20 n may not need to send the original data to the dataanalytics network element 100.

The data analytics network element 100 and the data analytics networkelement 201 to the data analytics network element 20 n may be deployedat a granularity of inter-operator (Inter-Public Land Mobile Network,Inter-PLMN), intra-operator or inter-region (Intra-PLMN orInter-Region), inter-network slice, intra-network slice, inter-vendor,intra-vendor, inter-data network name (data network name, DNN), orintra-DNN. In each granularity, there is a data analytics networkelement deployed in the server-client mode.

For example, if deployment is at a granularity of a vendor, at least onedata analytics network element 100 and one or more data analyticsnetwork elements are deployed in the vendor. For example, if thedeployment is at a granularity of a DNN, at least one data analyticsnetwork element 100 and one or more data analytics network elements aredeployed in the DNN.

Certainly, there is also a data analytics network element deployed at across granularity. For example, one data analytics network element 100is deployed in a same network slice, and one or more data analyticsnetwork elements are deployed in each of different network areas servedby the network slice.

In a possible implementation, the communication system shown in FIG. 1may be applied to a current 5G network architecture and another futurenetwork architecture. This is not specifically limited in thisembodiment of this application.

The following uses an example in which the communication system shown inFIG. 1 is applicable to the 5G network architecture. For example, thecommunication system shown in FIG. 1 is applicable to a 5G networkarchitecture shown in FIG. 2 .

For example, the communication system shown in FIG. 1 is applied to aninterface-based architecture in the 5G network architecture. As shown inFIG. 2 , a network element or an entity corresponding to any one of thedata analytics network element 100 or the data analytics network element201 to the data analytics network element 20 n may be a network dataanalytics function (NWDAF) network element in the 5G networkarchitecture shown in FIG. 2 , may be a management data analyticsfunction (MDAF) network element of a network management system, or mayeven be a data analytics network element or data analytics device on aRAN side.

Alternatively, a network element or an entity corresponding to any dataanalytics network element in this embodiment of this application may bethe NWDAF network element, the MDAF network element, or a module in thedata analytics network element or the data analytics device on the RANside. This is not limited in this embodiment of this application.

Certainly, the network element or the entity corresponding to any one ofthe data analytics network element 100 or the data analytics networkelement 201 to the data analytics network element 20 n may be a terminalshown in FIG. 2 .

It should be noted that the network element or the entity correspondingto any one of the data analytics network element 100 or the dataanalytics network element 201 to the data analytics network element 20 nis not limited to the terminal, the NWDAF network element, or the like.Any network element that has a model training function or supportsdistributed learning may be used as the data analytics network elementin this embodiment of this application.

A service discovery network element 300 supports a network function orfunctions of registration, discovery, update, and authentication of anetwork service. For example, a network element or an entitycorresponding to the service discovery network element 300 may be anetwork repository function (NRF) network element, a unified datamanagement (UDM) network element, or a unified data repository (UDR)network element in the 5G network architecture shown in FIG. 2 .Alternatively, the service discovery network element 300 may be a domainname system (DNS) server.

It should be noted that, in this embodiment of this application, anexample in which the service discovery network element 300 is the NRFnetwork element is used. In a future network, the service discoverynetwork element 300 may be the NRF network element or have another name.This is not limited in this application.

In addition, as shown in FIG. 2 , the 5G network architecture mayfurther include the terminal, an access device (for example, an accessnetwork (AN) or a radio access network (RAN)), an application function(AF) network element, an operation, administration, and maintenance(OAM) network element, a PCF network element, an SMF network element, auser plane function (UPF) network element, a data network (DN), an AMFnetwork element, an authentication server function (AUSF) networkelement, a network exposure function (NEF) network element, a UDRnetwork element, a UDM network element, or the like. This is notspecifically limited in this embodiment of this application.

The terminal communicates with the AMF network element through a nextgeneration network (next generation, N1) interface (N1 for short). Theaccess device communicates with the AMF network element through an N2interface (N2 for short). The access device communicates with the UPFnetwork element through an N3 interface (N3 for short). The UPF networkelement communicates with the DN through an N6 interface (N6 for short).The UPF network element communicates with the SMF network elementthrough an N4 interface (N4 for short). The AMF network element, theAUSF network element, the SMF network element, the UDM network element,the UDR network element, the NRF network element, the NEF networkelement, or the PCF network element interacts with each other through aservice-based interface. For example, an external service-basedinterface provided by the AMF network element may be Namf. An externalservice-based interface provided by the SMF network element may be Nsmf.An external service-based interface provided by the UDM network elementmay be Nudm. An external service-based interface provided by the UDRnetwork element may be Nudr. An external service-based interfaceprovided by the PCF network element may be Npcf. An externalservice-based interface provided by the NEF network element may be Nnef.An external service-based interface provided by the NRF network elementmay be Nnrf. An external service-based interface provided by the NWDAFnetwork element may be Nnwdaf. It should be understood that, for relateddescriptions of names of various service-based interfaces in FIG. 2 ,refer to a diagram of a 5G system architecture in a conventionaltechnology. Details are not described herein.

It should be understood that, an example in which some network elements(the AMF network element, the AUSF network element, the SMF networkelement, the UDM network element, the UDR network element, the NRFnetwork element, the NEF network element, and the PCF network element)in a 5GC interact with each other through the service-based interface isused in FIG. 2 . Certainly, the AMF network element may alternativelycommunicate with the SMF network element through an N11 interface (N11for short). The AMF network element may alternatively communicate withthe UDM network element through an N8 interface (N8 for short). The SMFnetwork element may alternatively communicate with the PCF networkelement through an N7 interface (N7 for short). The SMF network elementmay alternatively communicate with the UDM network element through anN10 interface (N10 for short). The AMF network element may alternativelycommunicate with the AUSF network element through an N12 interface (N12for short). The UDM network element may alternatively communicate withthe UDR network element through an interface between the UDM networkelement and the UDR network element. The PCF network element mayalternatively communicate with the UDR network element through aninterface between the PCF network element and the UDR network element.This is not limited in this embodiment of this application.

The AMF network element is mainly responsible for mobility management ina mobile network, such as user location update, registration of a userwith a network, and user switching.

The SMF network element is mainly responsible for session management inthe mobile network, such as session establishment, modification, andrelease. For example, specific functions are allocation of an IP addressfor the user and selection of a UPF network element that provides apacket forwarding function.

The PCF network element is configured to formulate a background traffictransfer policy.

The UDM network element or the UDR network element is configured tostore user data, for example, information about any data analyticsnetwork element.

The UPF network element is mainly responsible for processing a userpacket, such as forwarding and charging for the user packet.

The DN refers to an operator network that provides a data transmissionservice for the terminal, for example, an IP multimedia service (IMS) orInternet.

The data analytics network element is a network element device that canperform big data analytics, and may be but is not limited to a networkdata analytics function network element. For example, the network dataanalytics function network element may be the NWDAF. In this embodimentof this application, the data analytics network element can performdistributed learning based training or inference.

The NRF network element supports a network function or functions ofregistration, discovery, update, and authentication of a networkservice.

An application network element may be, but is not limited to, an AFnetwork element of the operator, a terminal, or a third-party device,for example, an AF network element of a non-operator (which may also bereferred to as a third-party AF network element). The AF network elementof the operator may be but is not limited to a service management andcontrol server of the operator, and the third-party AF network elementmay be but is not limited to a third-party service server.

Before embodiments of this application are described, related terms inembodiments of this application are explained as follows.

Federated Learning is an emerging basic artificial intelligencetechnology. It is designed to implement efficient machine learning amonga plurality of participants or computing nodes when ensuring informationsecurity during big data exchange, protecting terminal data and personaldata privacy, and ensuring legal compliance. Cross-domain joint modeltraining may be implemented when original data is not transmitted out ofa local domain, to improve training efficiency. Most importantly, thefederated learning technology may be used to avoid security problems(for example, the original data is hijacked during transmission orincorrectly used by a data center) caused by data aggregation to a dataanalytics center.

In detail, federated learning may be classified into the following threecategories.

Horizontal federated learning (Horizontal FL, or HFL): A featurerepetition rate is very high, but data samples differ from each othergreatly.

Vertical federated learning (VFL): A feature repetition rate is verylow, but a data sample repetition rate is high. For example, arepetition rate between a data feature from A and a data feature from Bin horizontal federated learning is higher than a repetition ratebetween a data feature from A and a data feature from B in verticalfederated learning.

Transfer learning (TL): Features and data samples differ greatly.

FIG. 3 describes a training process of horizontal federated learningaccording to an embodiment of this application by using linearregression as an example. It may be learned from FIG. 3 that horizontalfederation includes a central server node and a plurality of edge clientnodes (for example, a client node A, a client node B, and a client nodeC). Original data is distributed on each client node, the server nodedoes not have the original data, and the client node is not allowed tosend the original data to the server node.

First, a data set on each client node (assuming that there are K clientnodes in total, in other words, there are K data sets) is as follows:

{x_(i) ^(A),y_(i) ^(A)}_(i)∈D_(A), {x_(j) ^(B), y_(j) ^(B)}_(j)∈D_(B), .. . , {x_(k) ^(K), y_(k) ^(K)}_(k)∈D_(K),

where x is sample data, and y is label data corresponding to the sampledata. In horizontal federated learning, each piece of sample dataincludes a label, in other words, the label and the data are storedtogether.

Then, a data analytics module on each client node may train, based on alinear regression algorithm, a model that is of the client and that iscalled a sub-model:

h(x _(i))=Θ_(A) x _(i) ^(A) , h(x _(j))=Θ_(B) x _(i) ^(B) , . . . , h(x_(K))=Θ_(K) Kx _(k) ^(K).

It is assumed that a loss function used by linear regression is a meansquare error (MSE). In this case, a target function for training eachsub-model (where an entire training process is to minimize a lossfunction value) is:

${{\min L_{I}} = {{\sum\limits_{i}{{{\Theta_{I}x_{i}^{I}} - y_{i}^{I}}}^{2}} + {\frac{\lambda}{2}{\Theta_{I}}^{2}}}},{I = A},B,\ldots,K$

The training process really starts below. For each iteration process:

(1) A sub-model gradient generated by each client node is as follows:

${\frac{\partial L_{I}}{\partial\Theta_{I}} = {{\sum\limits_{i}{\left( {{\Theta_{I}x_{i}^{I}} - y_{i}^{I}} \right)x_{i}^{I}}} + {\lambda\Theta}_{I}}},{I = A},B,\ldots,K$

(2) Each client reports a quantity of samples and a local gradientvalue:

N_(I) and

$\frac{\partial L_{I}}{\partial\Theta_{I}},$

where N_(I) represents the quantity of samples, and

$\frac{\partial L_{I}}{\partial\Theta_{I}}$

represents the local gradient value.

(3) After receiving the foregoing information, the server nodeaggregates the gradient as follows:

${\frac{1}{K}{\sum\limits_{I}{\frac{\partial L_{I}}{\partial\Theta_{I}}*P_{I}}}},$

where ∥K∥ is a quantity of client nodes P_(I)=N_(I)/Σ_(I)N_(I).

(4) The server node delivers an aggregated gradient to each client nodethat participates in training, and then the client node locally updatesa model parameter as follows:

$\begin{matrix}{\Theta_{I}:={\Theta_{I} + {\alpha\frac{1}{K}{\sum\limits_{I}{\frac{\partial L_{I}}{\partial\Theta_{I}}*P_{I’}}}}}} & {{I = A},B,\ldots,K}\end{matrix}$

(5) After updating the model parameter, the client node calculates theloss function value L_(I) and goes to step 1.

In the foregoing training process, the server node may control, based ona quantity of iterations, the training to end, for example, terminatethe training when the training is performed for 10000 times, or control,by setting a threshold of the loss function, the training to end, forexample, control the training to end when L_(I)≤0.0001.

After training ends, each client node retains a same model (which may befrom the server node or may be obtained by locally personalizing basedon the server node) for local inference.

The foregoing describes the training process of horizontal federatedlearning. However, a current 5G network does not relate to how to applythe training process of horizontal federated learning to perform modeltraining, especially for a scenario described in FIG. 4 or FIG. 5 . Forexample:

Refer to FIG. 4 . Scenario 1: Intra-operator and inter-vendor. Forexample, a mobile operator A may simultaneously purchase a device from avendor X and a device from a vendor Y, but the device from the vendor Xand the device from the vendor Y cannot directly exchange data forprivacy protection. In other words, neither the device from the vendor Xnor the device from the vendor Y provides data collected by each deviceto a data analytics network element in the mobile operator A. In thiscase, although the data analytics network element (for example, aServer-type data analytics network element) in the mobile operator A maytrain a model of an entire network by using a federated learningtechnology, a prerequisite for performing federated learning technologytraining is that the data analytics network element can accurately learnof a network element or a device that supports horizontal federatedlearning in devices of different vendors (for example, each vendor has aClient-type data analytics network element that provides a service forthe vendor). Therefore, how the data analytics network element in themobile operator A discovers whether the devices of different vendorssupport horizontal federated learning is a problem that urgently needsto be resolved.

Refer to FIG. 5 . Scenario 2: Inter-operator and intra-network. Forexample, a mobile operator A and a mobile operator B share a basestation side resource (for example, a spectrum), and the two operatorswant to train an entire network model. Then, the mobile operator A andthe mobile operator B share a data analytics result with each other.However, the mobile operator A and the mobile operator B are unwillingto report original data, and the entire network model may be obtainedthrough training by using a federated learning technology. Inconclusion, how a data analytics network element in the mobile operatorA or a data analytics network element in the mobile operator B discoverswhether a network element or a device of the other party supportshorizontal federated learning is a problem that urgently needs to beresolved.

In another scenario in which there is no willingness to exchange theoriginal data, for example, in a same network slice (identified bysingle network slice selection support information (S-NSSAI)), theoriginal data cannot be exchanged between different network sliceinstances (NSIs). In a same region (for example, in China, regionsinclude Northeast China, North China, East China, Central South China,Northwest China, and Southwest China), the original data cannot beexchanged between different cities. If each NSI in the same networkslice corresponds to one data analytics network element, for example,the data analytics network element may serve the NSI, and each city indifferent cities in the same region may also correspond to one dataanalytics network element, for example, the data analytics networkelement may serve the city, in the same network slice, if the originaldata cannot be exchanged between different NSIs, or if data cannot beexchanged between different cities in the same region, the federatedlearning technology may be used to obtain a target model. However, aprerequisite for implementing federated learning is that a dataanalytics network element (a server type) can obtain information about adata analytics network element (a client type) that serves each NSI orinformation about a data analytics network element (a client type) thatserves each city. Otherwise, horizontal federated learning cannot beperformed.

Based on this, an embodiment of this application describes acommunication method with reference to FIG. 6 and FIG. 7A and FIG. 7B.By using the method, a first data analytics network element mayaccurately obtain information about one or more second data analyticsnetwork elements that support distributed learning.

The following describes in detail a communication method provided inembodiments of this application with reference to FIG. 1 to FIG. 5 .

It should be noted that names of messages between network elements,names of parameters in the messages, or the like in the followingembodiments of this application are only examples, and there may beother names in a specific implementation. This is not specificallylimited in embodiments of this application.

It should be noted that mutual learning or reference may be made betweenembodiments of this application. For example, mutual reference may bemade between same or similar steps or same or similar nouns, methodembodiments, communication system embodiments, and apparatusembodiments. This is not limited.

The following describes an interaction embodiment of a communicationmethod provided in embodiments of this application by using FIG. 6 andFIG. 7A and FIG. 7B as examples. The communication method may beperformed by a first data analytics network element, or may be performedby an apparatus (for example, a chip) used in a first data analyticsnetwork element. The communication method may be performed by a seconddata analytics network element, or may be performed by an apparatus (forexample, a chip) used in a second data analytics network element. Thecommunication method may be performed by a service discovery networkelement, or may be performed by an apparatus (for example, a chip) usedin a service discovery network element. The following embodiments aredescribed by using an example in which the communication method isperformed by the first data analytics network element, the second dataanalytics network element, and the service discovery network element. Itmay be understood that steps performed by the first data analyticsnetwork element may alternatively be performed by the apparatus used inthe first data analytics network element, steps performed by the seconddata analytics network element may alternatively be performed by theapparatus used in the second data analytics network element, and stepsperformed by the service discovery network element may alternatively beperformed by the apparatus used in the service discovery networkelement. Descriptions are centrally provided herein, and details are notdescribed subsequently.

For example, an example in which the communication method provided inembodiments of this application is applied to the communication systemsshown in FIG. 1 to FIG. 3 is used. FIG. 6 is a schematic interactiondiagram of a communication method according to an embodiment of thisapplication. The method includes the following steps.

Step 601: A first data analytics network element sends a first requestto a service discovery network element, and correspondingly, the servicediscovery network element receives the first request from the first dataanalytics network element. The first request requests information abouta second data analytics network element.

For example, the first request includes one or more of information aboutdistributed learning and first indication information. The informationabout distributed learning includes a type of distributed learning, andthe first indication information indicates a type of the second dataanalytics network element required by the first data analytics networkelement.

It should be understood that the type of distributed learning carried inthe first request is a type of distributed learning that the second dataanalytics network element requested by the first data analytics networkelement should have.

In this embodiment of this application, an example in which distributedlearning is federated learning is used. For example, the type ofdistributed learning includes one of horizontal learning, verticallearning, and transfer learning.

In a possible implementation, the first request may further carry fourthindication information, where the fourth indication informationindicates the first data analytics network element to request theinformation about the second data analytics network element from theservice discovery network element.

It may be understood that the second data analytics network elementrequested by the first data analytics network element from the servicediscovery network element by using the first request in step 601 in thisembodiment of this application may be a general term. In this case, thefirst data analytics network element may not know an identifier of thesecond data analytics network element. The first data analytics networkelement includes requirement information (for example, the informationabout distributed learning or the first indication information) aboutthe first data analytics network element in the first request, so thatthe service discovery network element provides, based on the requirementinformation, one or more second data analytics network elements thatmeet the requirement information for the first data analytics networkelement.

For example, the type of the second data analytics network element isone of the following: a client, a local trainer, or a partial trainer.

For example, the first data analytics network element may be the dataanalytics network element 100 shown in FIG. 1 . The service discoverynetwork element may be the service discovery network element 300.

Step 602: The service discovery network element determines the one ormore second data analytics network elements based on the first request.

It should be understood that the one or more second data analyticsnetwork elements determined by the service discovery network element instep 602 support the type of distributed learning requested by the firstdata analytics network element, and/or a type of the one or more seconddata analytics network elements is the same as the type of the seconddata analytics network element indicated by the first indicationinformation.

For example, the one or more second data analytics network elements maybe all or some of the data analytics network element 201 to the dataanalytics network element 20 n shown in FIG. 1 .

For example, if the type of distributed learning carried in the firstrequest is horizontal learning, the first data analytics network elementrequests a second data analytics network element that may performhorizontal learning, and the type of the second data analytics networkelement indicated by the first indication information is the client orthe local trainer, the type of distributed learning supported by the oneor more second data analytics network elements determined by the servicediscovery network element should be horizontal learning. In addition, inone aspect, in the one or more second data analytics network elements, atype of at least some second data analytics network elements is theclient, and a type of other second data analytics network elements isthe local trainer. Alternatively, in another aspect, the type of the oneor more second data analytics network elements is the client or thelocal trainer. This is not limited in this embodiment of thisapplication.

It may be understood that, if the type of the second data analyticsnetwork element indicated by the first indication information includes Aand B, or the type of the second data analytics network elementindicated by the first indication information includes A or B, the typeof at least some of the one or more second data analytics networkelements is A, and the type of the other second data analytics networkelements is B. For example, the one or more second data analyticsnetwork elements provided by the service discovery network element forthe first data analytics network element should not only include thesecond data analytics network elements of the type A, but should alsoinclude the second data analytics network elements of the type B.

In addition, when the type of the second data analytics network elementindicated by the first indication information includes A or B, the typeof the one or more second data analytics network elements may be all Aor all B. For example, the type of the one or more second data analyticsnetwork elements provided by the service discovery network element forthe first data analytics network element may be all B or all A. This isnot limited in this embodiment of this application.

It should be understood that, that the type of the second data analyticsnetwork element indicated by the first indication information includes Aand B does not mean that the type of the second data analytics networkelement requested by the first data analytics network element is both Aand B. In other words, the second data analytics network elementrequested by the first data analytics network element may be one of thetype A or the type B.

In an example, if the type of distributed learning includes a pluralityof horizontal learning, vertical learning, and transfer learning, theone or more second data analytics network elements may include a seconddata analytics network element supporting horizontal learning, a seconddata analytics network element supporting vertical learning, and asecond data analytics network element supporting transfer learning.

For example, the one or more second data analytics network elementsinclude a data analytics network element 201, a data analytics networkelement 202, and a data analytics network element 203. In this case, thedata analytics network element 201 may support horizontal learning, thedata analytics network element 202 may support vertical learning, andthe data analytics network element 203 may support transfer learning.

In another example, if the type of distributed learning includes theplurality of horizontal learning, vertical learning, and transferlearning, each of the one or more second data analytics network elementsneeds to support horizontal learning, vertical learning, and transferlearning.

It should be understood that the service discovery network element hasat least information about the one or more second data analytics networkelements, or the service discovery network element may obtaininformation about the one or more second data analytics network elementsfrom another device based on the first request. The information aboutthe one or more second data analytics network elements may be, forexample, one or more of the following information corresponding to thesecond data analytics network element: the information about distributedlearning, a range of the second data analytics network element, orsecond indication information, and the second indication informationindicates the type of the second data analytics network element. Theinformation about distributed learning corresponding to the second dataanalytics network element includes the type of distributed learningsupported by the second data analytics network element and/or algorithminformation of distributed learning supported by the second dataanalytics network element.

For example, the algorithm information supported by distributed learningin this embodiment of this application includes one or more of analgorithm type, an algorithm identifier (ID), and algorithm performanceDescriptions are centrally provided herein, and details are notdescribed subsequently.

For example, the algorithm type may be one or more of linear regression,logistic regression, a neural network, K-Means, reinforcement learning,and the like. The algorithm performance may be one or more of trainingtime, a convergence speed, and the like. The algorithm performance ismainly used to assist a data analytics network element in selecting,during model training, an algorithm whose algorithm performance ishigher than a preset algorithm threshold (for example, the training timeis less than a preset time threshold or the convergence speed is higherthan a preset speed threshold).

Step 603: The service discovery network element sends the informationabout the one or more second data analytics network elements to thefirst data analytics network element, and correspondingly, the firstdata analytics network element receives the information about the one ormore second data analytics network elements from the service discoverynetwork element.

It should be understood that types of different second data analyticsnetwork elements in the one or more second data analytics networkelements may be the same or different. Types of distributed learningsupported by different second data analytics network elements may be thesame or different. Algorithm information of distributed learningsupported by different second data analytics network elements may be thesame or different. This is not limited in this embodiment of thisapplication.

This embodiment of this application provides a communication method. Inthe method, the first data analytics network element sends the firstrequest to the service discovery network element, and requests, from theservice discovery network element by using the first request, a featureof the second data analytics network element required by the first dataanalytics network element. In this way, the service discovery networkelement provides, for the first data analytics network element based onthe first request, the information about the one or more second dataanalytics network elements that support the type of distributedlearning. In addition, the type of the second data analytics networkelement is the same as the type of the second data analytics networkelement requested by the first data analytics network element. In thissolution, in an aspect, the first data analytics network element canfind, by using the service discovery network element, a data analyticsnetwork element that can perform distributed learning based training Inanother aspect, after obtaining the information about the one or moresecond data analytics network elements, the first data analytics networkelement can subsequently collaborate with the one or more second dataanalytics network elements to implement model training when the firstdata analytics network element is to perform model training, so that adata analytics application scenario can be extended.

In a possible embodiment, before step 601, the method provided in thisembodiment of this application may further include: The first dataanalytics network element determines to trigger distributed learningbased training.

In an example, that the first data analytics network element determinesto trigger distributed learning based training may be implemented in thefollowing manner The first data analytics network element determines,based on configuration information or a manual indication, to triggerdistributed learning based training

In another example, that the first data analytics network elementdetermines to trigger distributed learning based training may beimplemented in the following manner The first data analytics networkelement actively initiates distributed learning based training.

In still another example, that the first data analytics network elementdetermines to trigger distributed learning based training may beimplemented in the following manner The first data analytics networkelement determines, based on a data analytics result request of aconsumer function (Consumer NF) network element, to trigger distributedlearning based training. For example, the Consumer NF network element isan SMF network element. If the SMF network element requests the firstdata analytics network element to perform service identification on adata packet flowing through a UPF network element, in this case, thefirst data analytics network element finds that a service identificationmodel has not been trained, and therefore triggers distributed learningbased training.

To enable the one or more second data analytics network elementsprovided by the service discovery network element for the first dataanalytics network element to meet a requirement of the first dataanalytics network element, in a possible embodiment, the first requestfurther includes a range of the first data analytics network element.Correspondingly, a range of the one or more second data analyticsnetwork elements provided by the service discovery network element forthe first data analytics network element falls within the range of thefirst data analytics network element. Step 602 in this embodiment ofthis application may be implemented in the following manner The servicediscovery network element uses, as the one or more second data analyticsnetwork elements, one or more second data analytics network elementsthat are located within the range of the first data analytics networkelement and that support the information about distributed learningrequested by the first data analytics network element. Alternatively,step 602 in this embodiment of this application may be implemented inthe following manner The service discovery network element uses, as theone or more second data analytics network elements, one or more seconddata analytics network elements that are located within the range of thefirst data analytics network element and whose type is the same as thetype of the second data analytics network element indicated by the firstindication information.

For example, the range of the first data analytics network elementincludes one or more of the following information: an area served by thefirst data analytics network element, a PLMN identifier to which thefirst data analytics network element belongs, information about anetwork slice served by the first data analytics network element, a datanetwork name (DNN) served by the first data analytics network element,and device vendor information of the first data analytics networkelement. The information about the network slice is used to identify thenetwork slice. For example, the information about the network slice maybe single network slice selection assistance information (S-NSSAI).

A range of the network slice served by the first data analytics networkelement may be used as the range of the first data analytics networkelement.

For example, the range of the second data analytics network elementincludes one or more of the following information: an area served by thesecond data analytics network element, a PLMN identifier to which thesecond data analytics network element belongs, a range of a networkslice instance served by the second data analytics network element, aDNN served by the second data analytics network element, and devicevendor information of the second data analytics network element.

In a possible embodiment, the information about distributed learning inthis embodiment of this application further includes the algorithminformation supported by distributed learning. Correspondingly, the oneor more second data analytics network elements provided by the servicediscovery network element for the first data analytics network elementfurther support the algorithm information supported by distributedlearning.

It should be understood that if the information about distributedlearning in the first request includes the type of distributed learningand the algorithm information supported by distributed learning, the oneor more second data analytics network elements provided by the servicediscovery network element for the first data analytics network elementshould not only support the type of distributed learning, but shouldalso support the algorithm information supported by distributedlearning.

For example, if the first data analytics network element uses the firstrequest to request the service discovery network element to search for asecond data analytics network element that supports horizontal learningand whose supported algorithm type is “linear regression”, the one ormore second data analytics network elements provided by the servicediscovery network element for the first data analytics network elementnot only support horizontal learning, but also support the algorithmtype “linear regression”.

In a possible example, the information about distributed learningcarried in the first request includes the type of distributed learningand the algorithm information supported by distributed learning. Inanother possible example, the information about distributed learningcarried in the first request includes the type of distributed learningand algorithm information supported by distributed learning, and thefirst request further carries the range of the first data analyticsnetwork element.

FIG. 7A and FIG. 7B show another possible embodiment according to anembodiment of this application. The method includes a registrationphase, a network element discovery phase, and a model training phase.The registration phase includes step 701 to step 704. The networkelement discovery phase includes step 705 to step 707. The modeltraining phase includes step 708 to step 714.

Step 701: A first data analytics network element sends a second requestto a service discovery network element, and correspondingly, the servicediscovery network element receives the second request from the firstdata analytics network element. The second request requests to registerinformation about the first data analytics network element.

The information about the first data analytics network element includesone or more of the following information corresponding to the first dataanalytics network element: information about distributed learning, arange of the first data analytics network element, or second indicationinformation. The second indication information indicates a type of thefirst data analytics network element.

The information about distributed learning corresponding to the firstdata analytics network element includes one or more of a type ofdistributed learning supported by the first data analytics networkelement and algorithm information supported by distributed learningsupported by the first data analytics network element. For example, thesecond request may be a registration request message.

In a possible implementation, the second request may further includefifth indication information, and the fifth indication informationrequests to register the information about the first data analyticsnetwork element.

For example, the type of the first data analytics network elementincludes one or more of the following information: a server, acoordinator, a centralized trainer, and a global trainer.

In a possible implementation, the information about the first dataanalytics network element may further include an identifier of the firstdata analytics network element and address information of the first dataanalytics network element.

Step 702: The service discovery network element registers theinformation about the first data analytics network element.

In a possible implementation, step 702 in this embodiment of thisapplication may be implemented in the following manner The servicediscovery network element registers the information about the first dataanalytics network element with the service discovery network element.For example, the service discovery network element stores theinformation about the first data analytics network element in a storagedevice of the service discovery network element.

In a possible implementation, step 702 in this embodiment of thisapplication may be implemented in the following manner The servicediscovery network element sends the information about the first dataanalytics network element to an external storage device (for example, aUDM network element or a UDR network element). Therefore, the externalstorage device stores the information about the first data analyticsnetwork element. Subsequently, the service discovery network element mayobtain the information about the first data analytics network elementfrom the external storage device.

In a possible implementation, the service discovery network element inthis embodiment of this application may alternatively be the UDM networkelement or the UDR network element. In other words, the UDM networkelement or the UDR network element stores the information about thefirst data analytics network element.

It should be noted that the first data analytics network elementregisters the information about the first data analytics network elementwith the service discovery network element. In this way, a Consumer NFnetwork element may subsequently query, by using the service discoverynetwork element, information about a first data analytics networkelement that supports distributed learning and whose type is the serveror the coordinator. Then, the Consumer NF network element requests, fromthe first data analytics network element, to perform serviceidentification on a data packet that flows through a UPF networkelement.

Step 703: The second data analytics network element sends a thirdrequest to the service discovery network element, and correspondingly,the service discovery network element receives the third request fromthe second data analytics network element. The third request requests toregister information about the second data analytics network element.

The information about the second data analytics network element includesone or more of the following information corresponding to the seconddata analytics network element: information about distributed learning,a range of the second data analytics network element, or firstindication information, and the first indication information indicates atype of the second data analytics network element. The information aboutdistributed learning corresponding to the second data analytics networkelement may include one or more of a type of distributed learningsupported by the second data analytics network element and algorithminformation of distributed learning supported by the second dataanalytics network element.

In a possible implementation, the third request may further includesixth indication information, and the sixth indication informationrequests to register the information about the second data analyticsnetwork element.

In a possible implementation, the information about the second dataanalytics network element may further include an identifier of thesecond data analytics network element and address information of thesecond data analytics network element.

Step 704: The service discovery network element registers theinformation about the second data analytics network element.

For implementation of step 704, refer to the descriptions in step 702.Details are not described herein again. A difference lies in that theservice discovery network element registers the information about thesecond data analytics network element.

It may be understood that each of the one or more second data analyticsnetwork elements may register information about each second dataanalytics network element with the service discovery network element.

In this embodiment of this application, step 701 and step 702 are aprocess in which the first data analytics network element registers theinformation about the first data analytics network element with theservice discovery network element, step 703 and step 704 are a processin which the second data analytics network element registers theinformation about the second data analytics network element with theservice discovery network element, and a perform sequence of step 701and step 702 and step 703 and step 704 is not limited herein.

Information about whether to register a data analytics network element(for example, the first data analytics network element or the seconddata analytics network element) with the service discovery networkelement may be autonomously determined by the data analytics networkelement, or determined by a protocol, or another network elementtriggers the data analytics network element to perform a registrationprocess. This is not limited in this embodiment of this application.

Step 705 to step 707 are the same as step 601 to step 603, and detailsare not described herein again.

In a possible embodiment, as shown in FIG. 7A and FIG. 7B, after step707, the method provided in this embodiment of this application mayfurther include the following steps.

Step 708: The first data analytics network element determines, based onthe information about the one or more second data analytics networkelements, information about a third data analytics network element thatcan perform distributed learning, where there is one or more third dataanalytics network elements.

In this embodiment of this application, the one or more third dataanalytics network elements may be all or some of the one or more seconddata analytics network elements.

For example, the one or more second data analytics network elements arethe data analytics network element 201 to the data analytics networkelement 20 n. In this case, the one or more third data analytics networkelements may be a data analytics network element 201, a data analyticsnetwork element 202, and a data analytics network element 203.

The third data analytics network element meets any one of the followingconditions in Example 1 to Example 3:

EXAMPLE 1 Load of the Third Data Analytics Network Element is Lower thana Preset Load Threshold

It may be understood that step 708 may be implemented in the followingmanner The first data analytics network element obtains load informationof the one or more second data analytics network elements. The firstdata analytics network element determines, based on the load informationof the one or more second data analytics network elements, a second dataanalytics network element whose load is less than the preset loadthreshold in the one or more second data analytics network elements asthe third data analytics network element that can perform distributedlearning.

EXAMPLE 2 A Priority of the Third Data Analytics Network Element isHigher than a Preset Priority Threshold

It may be understood that step 708 may be implemented in the followingmanner The first data analytics network element obtains one or morepriorities of the one or more second data analytics network elements.The first data analytics network element determines, based on the one ormore priorities of the one or more second data analytics networkelements, a second data analytics network element whose priority ishigher than the preset priority threshold in the one or more second dataanalytics network elements as the third data analytics network elementthat can perform distributed learning.

EXAMPLE 3 The Third Data Analytics Network Element is Located Within theRange of the First Data Analytics Network Element

It may be understood that step 708 may be implemented in the followingmanner The first data analytics network element obtains one or moreranges of the one or more second data analytics network elements. Thefirst data analytics network element determines, based on the range ofthe one or more second data analytics network elements, a second dataanalytics network element that is located within the range of the firstdata analytics network element as the third data analytics networkelement that can perform distributed learning.

If the first request does not carry the range of the first dataanalytics network element, some of the one or more second data analyticsnetwork elements provided by the service discovery network element forthe first data analytics network element may be located outside therange of the first data analytics network element, and other second dataanalytics network elements may be located within the range of the firstdata analytics network element. Therefore, after obtaining theinformation about the one or more second data analytics networkelements, the first data analytics network element may further performscreening based on location information of each second data analyticsnetwork element to obtain one or more third data analytics networkelements located within the range of the first data analytics networkelement.

If the first request carries the range of the first data analyticsnetwork element, the one or more second data analytics network elementsprovided by the service discovery network element for the first dataanalytics network element are located within the range of the first dataanalytics network element, and undoubtedly, the one or more third dataanalytics network elements are also located within the range of thefirst data analytics network element.

It should be noted that Example 1, Example 2, and Example 3 may beseparately used, or may be used in combination, to serve as a conditionfor the first data analytics network element to determine the third dataanalytics network element from the one or more second data analyticsnetwork elements. When Example 1, Example 2, and Example 3 are used incombination, the load of the third data analytics network element is notonly lower than the preset load threshold, but also has a priorityhigher than the preset priority threshold, and the third data analyticsnetwork element is also located within the range of the first dataanalytics network element.

Example 1 to Example 3 are only examples in which the first dataanalytics network element determines the third data analytics networkelement from the one or more second data analytics network elements. Inthis embodiment of this application, the first data analytics networkelement may alternatively determine the third data analytics networkelement from the one or more second data analytics network elements inanother manner. This is not limited in this embodiment of thisapplication.

Step 709: Each of the one or more third data analytics network elementsdetermines a sub-model (Sub-Model). The sub-model determined by anythird data analytics network element is obtained by the third dataanalytics network element through training based on data obtained by thethird data analytics network element.

The data obtained by the third data analytics network element refers todata obtained by the third data analytics network element from a rangeof the third data analytics network element. For example, the third dataanalytics network element obtains terminal data (from a UE), servicedata (from an AF network element), network data (from a core networkelement, for example, an AMF network element, an SMF network element, aPCF network element, or a UPF network element), base station data (froman access network element, for example, a RAN or a gNB), and networkmanagement data (from an OAM network element) from one or more (ConsumerNF) network elements within the range of the third data analyticsnetwork element. An example of the data obtained from each networkelement is shown in Table 1.

TABLE 1 Data obtained by the third data analytics network element fromanother network element Data type Network name Description Identifier ofa terminal AMF network Identifying the terminal element/SMF networkelement/RAN Location information AMF network element/RAN >Time(Timestamp) AMF network Time at which the terminal is in a element/RANlocation >Location Location of the terminal at a corresponding timeCommunication pattern SMF network information element/UPF networkelement >Communication start time SMF network Time at whichcommunication element/UPF network between the terminal and the SMFelement network element starts >Communication end time Time at whichcommunication between the terminal and the SMF network elementends >Registration time AMF network element Time at which the terminalregisters with the AMF network element >Deregistration time Time atwhich the terminal deregisters from the AMF network element >Sessionestablishment time SMF network element Time at which the terminal (PDUSession Establishment establishes a PDU session Time) >Sessionmodification time Time at which the PDU session of (PDU SessionModification the terminal is modified Time) >Session release time (PDUTime at which the PDU session of Session release Time) the terminal isreleased Network configuration SMF network information element/UPFnetwork element/RAN >(Uplink or downlink) data SMF network Indicating anend-to-end (between packet delay element/UPF network the terminal and aservice server or element/RAN a DN) delay of an uplink or downlink datapacket >(Uplink or downlink) data Indicating a size of a buffered packetsize uplink data packet or a buffered downlink data packet >(Uplink ordownlink) data Indicating a quantity of buffered packet quantity (UL orDL uplink data packets or buffered Packet Number) downlink datapackets >(Uplink or downlink) data Indicating time at which packettransfer start time transmission of an uplink data packet or a downlinkdata packet starts >(Uplink or downlink) data Indicating time at whichpacket transfer end time transmission of an uplink data packet or adownlink data packet ends >RRC connection AMF network Indicating time atwhich a radio establishment time element/RAN resource control (RadioResource Control, RRC) connection of the terminal is established >RRCconnection release time Indicating time at which the RRC connection ofthe terminal is released At least one piece of first-type AMF networkelement Other terminal behavior data on the data on the AMF network AMFnetwork element element (normalized UE behavioral data from AMF) Atleast one piece of first-type SMF network element Other terminalbehavior data on the data on the SMF network SMF network element element(normalized UE behavioral data from SMF) At least one piece offirst-type UPF network element Other terminal behavior data on the dataon the UPF network UPF network element element (normalized UE behavioraldata from UPF) At least one piece of first-type RAN Other terminalbehavior data on the data on the RAN (normalized RAN UE behavioral datafrom RAN) UE or DE data rate UPF network Indicating an uplink data rateor a element/SMF network downlink data rate of a data flow of elementthe terminal Reference signal received RAN/UE Indicating radio channelquality power (RSRP) RSRP of the terminal Roaming Information UEIncluding one or more of roaming status indication information, a timeperiod, a home PLMN, a visited PLMN, a home area, a visited area, aradio access technology type (RAT Type), and the like when the UE is ina roaming status. Reference signal received RAN/UE Indicating radiochannel quality quality (RSRQ) RSRQ of the terminal Signal tointerference plus RAN/UE Indicating radio channel quality noise ratio(SINR) SINR of the terminal Foreground application UE Foreground trafficidentifier identifier (Foreground Application ID) Traffic data switchstate (PS UE Traffic switch state on network of Data Off) an operator:on or off Service experience AF network Service experience of using aelement/UE service by the terminal (for example, a mean opinion score(Mean Opinion Score, MOS) of a voice service)

It should be understood that the third data analytics network elementmay determine the sub-model under triggering of the first data analyticsnetwork element.

In a possible implementation, step 709 in this embodiment of thisapplication may be implemented in the following manner Any third dataanalytics network element sets, based on a configuration parameter, aparameter used by the third data analytics network element to train theobtained data, and after the setting, trains, based on data (as shown inTable 1) obtained through training by a local intelligent chip (forexample, a graphics processing unit (GPU)) of the third data analyticsnetwork element, to obtain the sub-model. For example, for a trainingprocess, refer to the horizontal federated training process by using thelinear regression algorithm as an example in FIG. 3 . A trainingarchitecture of another algorithm is similar, and details are notdescribed herein again.

The configuration parameter in this embodiment of this application maybe preconfigured in the third data analytics network element, or theconfiguration parameter may be provided by the first data analyticsnetwork element.

If the configuration parameter is provided by the first data analyticsnetwork element for the one or more third data analytics networkelements, before step 709, the method provided in this embodiment ofthis application may further include: The first data analytics networkelement sends the configuration parameter to the one or more third dataanalytics network elements, and correspondingly, the one or more thirddata analytics network elements receive the configuration parameter fromthe first data analytics network element. The configuration parameter isused by the third data analytics network element to train the sub-model.

It should be noted that the first data analytics network element sendsthe configuration parameter to each of the one or more third dataanalytics network elements.

For example, the configuration parameter includes one or more of thefollowing information: an initial model, a training set selectioncriterion, a feature generation method, a training terminationcondition, maximum training time, or maximum waiting time.

For example, the initial model includes an algorithm type and an initialmodel parameter. The training set selection criterion is a limitationfor each feature. For example, during training of a service experiencemodel, RSRP measured by the terminal should be limited. When an RSRPvalue is less than −130 dB or greater than −100 dB, corresponding sampledata should be discarded. The feature generation method is a calculationmethod for each feature. For example, during training of the serviceexperience model, the RSRP is to be normalized from 0 to 1. In thiscase, the first data analytics network element indicates the third dataanalytics network element to normalize RSRP, for example, normalize amaximum value and a minimum value. Training termination condition: Forexample, a maximum quantity of iterations. Training is terminated whenthe quantity of iterations reaches a maximum quantity of iterations. Foranother example, a maximum loss function value. The loss functiondecreases in each round of iterative training, and the training may beterminated when the loss function decreases to a required maximum lossfunction value. The maximum training time is used to indicate maximumtime of each round of iterative training. When time of one round ofiterative training exceeds the maximum training time, an entirefederated training process may be affected. Therefore, the first dataanalytics network element limits time of each round of iterativetraining performed on the third data analytics network element. Themaximum waiting time is used to indicate maximum time at which the firstdata analytics network element waits for the third data analyticsnetwork element to feed back the sub-model during each round ofiterative training. If the time at which the first data analyticsnetwork element waits for the third data analytics network element tofeed back the sub-model during one round of iterative training exceedsthe maximum waiting time, the entire federated training process may beaffected. Therefore, the first data analytics network element limits thetime of each round of iterative training performed on the third dataanalytics network element.

Note: A transmission time is further required for transmitting thesub-model from the third data analytics network element to the firstdata analytics network element. Therefore, the maximum waiting timeincludes the maximum training time and the transmission time.

Step 710: The one or more third data analytics network elements sendrespective sub-models to the first data analytics network element, andcorrespondingly, the first data analytics network element receives thesub-models from the one or more third data analytics network elements.

Step 711: The first data analytics network element determines an updatedmodel based on the sub-model of the one or more third data analyticsnetwork elements.

It should be understood that the first data analytics network elementmay obtain an updated model by aggregating the sub-model provided byeach third data analytics network element.

For example, the one or more third data analytics network elements arethe data analytics network element 201, the data analytics networkelement 202, and the data analytics network element 203. A sub-modelprovided by the data analytics network element 201 is a sub-model 1, asub-model provided by the data analytics network element 202 is asub-model 2, and a sub-model provided by the data analytics networkelement 203 is a sub-model 3. In this case, the first data analyticsnetwork element may aggregate the sub-model 1, the sub-model 2, and thesub-model 3 to obtain the updated model.

Step 712: The first data analytics network element sends the updatedmodel to the one or more third data analytics network elements, andcorrespondingly, each of the one or more third data analytics networkelements may obtain the updated model from the first data analyticsnetwork element.

It should be understood that, after obtaining the updated model, thethird data analytics network element may perform a next round ofiterative training, to obtain a sub-model corresponding to the nextround of iteration. In other words, after step 712 is performed, step709 is performed cyclically until the training termination conditionindicated by the configuration parameter is met.

It should be understood that, if the maximum quantity of iterations inthe training termination condition is N, the third data analyticsnetwork element may perform N rounds of iterative training, and in eachround of iterative training, the third data analytics network elementsends a sub-model obtained through training to the first data analyticsnetwork element in a current round of iteration training.

In a possible embodiment, after step 712, the method provided in thisembodiment of this application may further include:

Step 713: The first data analytics network element determines a targetmodel based on the updated model.

For example, step 713 in this embodiment of this application may beimplemented in the following manner If determining that a set maximumquantity of federated training times (which may also be referred to as amaximum quantity of iterations) is reached, the first data analyticsnetwork element determines the updated model as the target model. Inother words, when the maximum quantity of training times is reached, thefirst data analytics network element determines the updated model as thetarget model.

It should be noted that the maximum quantity of federated training timesis a quantity of times in which the first data analytics network elementaggregates the sub-models. The maximum quantity of iterations in thetraining termination condition is a quantity of iterations in a processin which the third data analytics network element generates thesub-model before reporting the sub-model each time.

Step 714: The first data analytics network element sends, to the one ormore second data analytics network elements, the target model and one ormore of the following information corresponding to the target model: amodel identifier (model ID), a model version identifier (Version ID), ora data analytics identifier (analytics ID).

In some embodiments, before step 714, the method may further include:The first data analytics network element allocates the model identifier,the model version identifier, or the data analytics identifier for thetarget model.

FIG. 8 shows a detailed embodiment of a communication method accordingto an embodiment of this application by using an example in which a typeof a first data analytics network element is a server, the first dataanalytics network element may be referred to as a server NWDAF, a typeof a second data analytics network element is a client, the second dataanalytics network element may be referred to as a client NWDAF, aservice discovery network element is an NRF network element, and a typeof distributed learning is horizontal federated learning. The methodincludes the following steps.

Step 801: The server NWDAF triggers a network element management_networkelement registration request service operation(Nnrf_NFManagement_NFRegister Request) to the NRF network element, andcorrespondingly, the NRF network element receives the network elementmanagement network element registration request service operation fromthe server NWDAF.

The network element management network element registration requestservice operation requests to register information about the serverNWDAF with the NRF network element. The information about the serverNWDAF includes one or more of the following information: network elementbasic information, a range of the server NWDAF, federated learningcapability information, or second indication information.

It may be understood that, after receiving the information about theserver NWDAF, the NRF network element stores the information about theserver NWDAF, to complete registration of the information about theserver NWDAF.

In a possible implementation, the network element management_networkelement registration request service operation may carry indicationinformation that indicates to register the information about the serverNWDAF with the NRF network element.

Step 802: The NRF network element triggers a network elementmanagement_network element registration response service operation(Nnrf_NFManagement_NFRegister Response) to the server NWDAF, andcorrespondingly, the server NWDAF receives the network elementmanagement_network element registration response service operation fromthe NRF network element.

The network element management_network element registration responseservice operation indicates that the NRF network element hassuccessfully registered the information about the server NWDAF with theNRF network element. In a possible implementation, the network elementmanagement_network element registration response service operationcarries a successful registration indication, and the successfulregistration indication indicates that the NRF network element hassuccessfully registered the information about the server NWDAF with theNRF network element.

Step 803: The client NWDAF triggers the network elementmanagement_network element registration request service operation to theNRF network element, and correspondingly, the NRF network elementreceives the network element management_network element registrationrequest service operation from the client NWDAF.

The network element management_network element registration requestservice operation requests to register information about the clientNWDAF with the NRF network element. For example, the information aboutthe client NWDAF includes one or more of the following information:basic information about the client NWDAF, a range of the client NWDAF,federated learning capability information of the client NWDAF, or thirdindication information.

The basic information about the client NWDAF may be a type of the clientNWDAF, an identifier of the client NWDAF (for example, a client NWDAFID), a location of the client NWDAF, or address information of theclient NWDAF.

It may be understood that, after receiving the information about theclient NWDAF, the NRF network element stores the information about theclient NWDAF, to complete registration of the information about theclient NWDAF.

In a possible implementation, the network element management_networkelement registration request service operation may carry indicationinformation that indicates to register the information about the clientNWDAF with the NRF network element.

Step 804: The NRF network element triggers a network elementmanagement_network element registration response service operation(Nnrf_NFManagement_NFRegister Response) to the client NWDAF, andcorrespondingly, the client NWDAF receives the network elementmanagement_network element registration response service operation fromthe NRF network element.

The network element management_network element registration responseservice operation indicates that the NRF network element hassuccessfully registered the information about the client NWDAF with theNRF network element. In a possible implementation, the network elementmanagement_network element registration response service operationcarries a successful registration indication, and the successfulregistration indication indicates that the NRF network element hassuccessfully registered the information about the client NWDAF with theNRF network element.

Step 805: The server NWDAF determines to trigger horizontal federatedlearning based training.

For implementation of step 805, refer to the foregoing process in whichthe first data analytics network element determines to triggerdistributed learning based training. Details are not described herein.

Step 806: The server NWDAF requests, from the NRF network element, afirst client NWDAF list that can perform horizontal federated learning.

In an example, step 806 in this embodiment of this application may beimplemented in the following manner The server NWDAF triggers a networkelement discovery request (Nnrf_NFDiscovery_Request) to the NRF networkelement, and correspondingly, the NRF network element receives thenetwork element discovery request from the server NWDAF. The networkelement discovery request requests, from the NRF network element, thefirst client NWDAF list that can perform horizontal federated learning.

For example, the network element discovery request includes the range ofthe server NWDAF and first indication information.

It may be understood that the first indication information indicates, tothe NRF network element, a type of a client NWDAF or an algorithmperformance requirement that is required by the server NWDAF.

In a possible implementation, the network element discovery requestcarries indication information y, where the indication information yindicates to request, from the NRF network element, the first clientNWDAF list that can perform horizontal federated learning.

Step 807: The NRF network element determines the first client NWDAF listthat can perform horizontal federated learning. The first client NWDAFlist includes information about each client NWDAF in a client NWDAF 1 toa client NWDAF n.

For example, as shown in FIG. 10 , a PLMN corresponding to the clientNWDAF 1 is a PLMN 1, a TA is a TA 1, a slice instance is a sliceinstance 1, a device vendor of the client NWDAF 1 is a device vendor 1,a DNAI of the client NWDAF 1 is a DNAI 1, a PLMN corresponding to aclient NWDAF 2 is a PLMN 2, the TA is a TA 2, the slice instance is aslice instance 2, and a device vendor of the client NWDAF 2 is a devicevendor 2, and a DNAI of the client NWDAF 2 is DNAI 2. By analogy, theinformation about each client NWDAF is obtained.

Step 808: The NRF network element sends the first client NWDAF list tothe server NWDAF, and correspondingly, the server NWDAF receives thefirst client NWDAF list from the NRF network element.

It may be understood that the first client NWDAF list includes one ormore client NWDAFs that meet a requirement of the server NWDAF.

In a possible implementation, step 808 may be implemented in thefollowing manner The NRF network element sends a network elementdiscovery response to the server NWDAF, where the network elementdiscovery response includes the first client NWDAF list.

It may be understood that, before step 808, the method provided in thisembodiment of this application may further include: The NRF networkelement queries, based on the request of the server NWDAF on the NRFnetwork element, the client NWDAF 1 to the client NWDAF n that meet therequest of the server NWDAF, to obtain the first client NWDAF list.

Step 809: The server NWDAF determines load information of each clientNWDAF in the first client NWDAF list.

In a possible implementation, step 808 in this embodiment of thisapplication may be implemented in the following manner The server NWDAFqueries, from an OAM network element, the NRF network element, or anNWDAF that can analyze the load information of the client NWDAF, theload information of each client NWDAF in the first client NWDAF list.

For example, the load information of the client NWDAF corresponds to oneor more of the following information:

Status (registered, suspended, and undiscoverable);

NF resource usage (for example, a central processing unit (CPU), amemory, and hard disk);

NF load (Load, an actual value, an average value, or variance); and

NF peak load.

It may be understood that the load information of the client NWDAF instep 808 may alternatively be replaced with a priority of the clientNWDAF.

Step 810: The server NWDAF determines, based on the load information ofeach client NWDAF, a second client NWDAF list that can performhorizontal federated learning.

The second client NWDAF list includes information about all or some ofclient NWDAFs in a client NWDAF 1 to a client NWDAF n.

In an implementation, step 810 in this embodiment of this applicationmay be implemented in the following manner Load of a client NWDAFincluded in the second client NWDAF list is less than a preset loadthreshold.

For example, the server NWDAF sorts the first client NWDAF list inascending order of Load, and then selects a client NWDAF whose Load isless than the preset load threshold to perform horizontal federatedlearning. An objective of this action is to ensure that the selectedclient NWDAF has abundant resources for training a sub-model, to improvetraining efficiency of entire federated learning.

In an alternative implementation, step 810 in this embodiment of thisapplication may be replaced in the following manner The server NWDAFdetermines, based on a priority of each client NWDAF, the second clientNWDAF list that can perform horizontal federated learning. In this case,the priority of the client NWDAF included in the second client NWDAFlist is higher than a preset priority threshold.

In an implementation, each client NWDAF has a corresponding priority,and algorithm performance of a client NWDAF with a high priority ishigher than algorithm performance of a client NWDAF with a low priority,or algorithm performance of a client NWDAF with a high priority ishigher than a preset algorithm performance threshold. Alternatively,load of a client NWDAF with a high priority is lower than load of aclient NWDAF with a low priority, or load of a client NWDAF with a highpriority is lower than the preset load threshold. An algorithmperformance evaluation indicator of the client NWDAF with the highpriority is higher than an algorithm performance evaluation indicator ofthe client NWDAF with the low priority, or an algorithm performanceevaluation indicator of the client NWDAF with the high priority meets apreset algorithm performance evaluation indicator threshold. Thealgorithm performance evaluation indicator may include a square error,accuracy, a recall rate, and an F-Score (an average score obtained afterthe accuracy and the recall are reconciled).

In the embodiment shown in FIG. 8 , the server NWDAF or the client NWDAFregisters the federated capability information that the server NWDAF orthe client NWDAF has with the NRF network element, to assist a 5Gnetwork (for example, the server NWDAF) in finding, by using the NRFnetwork element, a proper client NWDAF for federated training ifhorizontal federated learning is to be performed.

The network element management_network element registration requestservice operation in step 801 in the embodiment shown in FIG. 8corresponds to the foregoing second request. The network elementmanagement_network element registration request service operation instep 803 corresponds to the foregoing third request. A coverage range ofthe server NWDAF may correspond to the range of the first data analyticsnetwork element in the foregoing embodiments. The range of the clientNWDAF may correspond to the range of the second data analytics networkelement in the foregoing embodiments. The network element discoveryrequest in step 806 corresponds to the first request in the foregoingembodiments. The client NWDAF 1 to the client NWDAF n correspond to theone or more second data analytics network elements in the foregoingembodiments. All client NWDAFs included in the second client NWDAF listcorrespond to the one or more third data analytics network elements inthe foregoing embodiments.

FIG. 9A and FIG. 9B show an embodiment of a model training methodaccording to an embodiment of this application. In the method, anexample in which a server NWDAF determines that clients NWDAFs thatperform horizontal federated training are a client NWDAF 1 and a clientNWDAF 3 is used. The method includes the following steps.

Step 901: The server NWDAF sends a configuration parameter to the clientNWDAF 1, and correspondingly, the client NWDAF 1 receives theconfiguration parameter from the server NWDAF. The configurationparameter is a parameter used by the client NWDAF 1 to train asub-model.

For example, step 901 may be implemented in the following manner Theserver NWDAF triggers an Nnwdaf_HorizontalFL_Create request serviceoperation to the client NWDAF 1, and correspondingly, the client NWDAF 1receives the Nnwdaf_HorizontalFL_Create request service operation fromthe server NWDAF.

The Nnwdaf_HorizontalFL_Create request service includes theconfiguration parameter. For example, for content of the configurationparameter, refer to the descriptions in the foregoing embodiments.Details are not described herein again.

Step 902: The server NWDAF sends the configuration parameter to theclient NWDAF 3, and correspondingly, the client NWDAF 3 receives theconfiguration parameter from the server NWDAF. The configurationparameter is a parameter used by the client NWDAF 3 to train asub-model.

It may be understood that, after receiving the configuration parameter,the client NWDAF 3 or the client NWDAF 1 may further send a responseindication to the server NWDAF, where the response indication indicatesthat the client NWDAF successfully configures the parameter used by theclient NWDAF to train the sub-model.

Step 903: The client NWDAF 1 or the client NWDAF 3 performs a trainingprocess based on data that is obtained by the client NWDAF 1 or theclient NWDAF 3 and the configuration parameter, to obtain a sub-model.

It may be understood that, in each sub-model reporting process of theclient NWDAF 1 or the client NWDAF 3, the client NWDAF 1 or the clientNWDAF 3 may perform a plurality of rounds of sub-iteration traininginternally. Each round of sub-iteration training corresponds to amaximum quantity of sub-iterations. The client NWDAF 1 or the clientNWDAF 3 may use, as the sub-model, a model obtained when the maximumquantity of sub-iterations corresponding to each round of sub-iterationtraining is reached.

Step 904: The client NWDAF 1 sends, to the server NWDAF, the sub-modelobtained by the client NWDAF 1 through training

For example, the client NWDAF 1 triggers an Nnwdaf_HorizontalFL_Updaterequest service operation to the server NWDAF, to send, to the serverNWDAF, the sub-model obtained by the client NWDAF 1 through training

Step 905: The client NWDAF 3 sends, to the server NWDAF, the sub-modelobtained by the client NWDAF 3 through training

For example, the client NWDAF 3 triggers the Nnwdaf_HorizontalFL_Updaterequest service operation to the server NWDAF, to send, to the serverNWDAF, the sub-model obtained by the client NWDAF 3 through training

The sub-model may be a black box, and is sent to the server NWDAF as amodel file. The sub-model may further be defined, including an algorithmtype, a model parameter, and the like.

In a possible implementation, after providing, to the server NWDAF, thesub-model obtained through respective training, the client NWDAF 1 orthe client NWDAF 3 may further request an updated model from the serverNWDAF.

As shown in FIG. 10 , the client NWDAF 3 sends, to the server NWDAF, asub-model 3 obtained by the client NWDAF 3 through training, and theclient NWDAF 1 sends, to the server NWDAF, a sub-model 1 obtained by theclient NWDAF 1 through training.

Step 906: The server NWDAF aggregates the sub-model obtained by theclient NWDAF 1 through training and the sub-model obtained by the clientNWDAF 3 through training, to obtain an updated model after a currentround of iteration.

Step 907: The server NWDAF sends the updated model to the client NWDAF 1and the client NWDAF 3.

It may be understood that each client NWDAF performs a plurality ofrounds of iterative training, and each client NWDAF in each round ofiterative training obtains, through training, a sub-model correspondingto a current round of iterative training. After the sub-model isobtained through each round of iteration training, each client NWDAFreports, to the server NWDAF, the sub-model corresponding to the currentround of iteration training.

Step 903 to step 907 may be cyclically performed until a trainingtermination condition set when the client NWDAF 1 and the client NWDAF 3perform sub-model training is met.

Step 908: After determining that federated training is terminated, theserver NWDAF determines a target model based on the updated model.

Step 909: The server NWDAF may allocate a version identifier (VersionID) and/or an analytics result type identifier (analytics ID)corresponding to the target model (which is referred to as TrainedModel, Global Model, or Optimal Model).

Step 910: The server NWDAF sends the target model, the versionidentifier and the analytics result type identifier corresponding to thetarget model, to all or some client NWDAFs within a range of the serverNWDAF.

For example, the server NWDAF triggers an Nnwdaf_HorizontalFL_UpdateAcknowledge service operation to all or some client NWDAFs within therange of the server NWDAF, to send the target model and the versionidentifier Version ID and the analytics result type identifier analyticsID that correspond to the target model to all or some client NWDAFswithin the range of the server NWDAF.

As shown in FIG. 10 , the server NWDAF sends, to the client NWDAF 1 to aclient NWDAF n, the target model and at least one of the modelidentifier Model ID, the version identifier Version ID, and theanalytics result type identifier analytics ID that correspond to thetarget model.

It should be noted that, although during model training, the clientNWDAF 1 and the client NWDAF 3 within the range of the server NWDAFparticipate in training, and another client NWDAF other than the clientNWDAF 1 and the client NWDAF 3 in the range of the server NWDAF does notparticipate in training, the another client NWDAF may still share thetarget model.

Step 911: The client NWDAF 1 and the client NWDAF 3 send, to an NEFnetwork element, the target model and at least one of the modelidentifier Model ID, the version identifier Version ID, and theanalytics result type identifier analytics ID that correspond to thetarget model.

For example, the client NWDAF 1 and the client NWDAF 3 separatelytrigger an Nnrf_NFManagement_NFRegister_request service operation to anNRF network element to register the analytics ID, the version ID, and avalid range (an area, a time period, and the like) corresponding to thetarget model, to notify the NRF network element that the client NWDAF 1and the client NWDAF 3 support analytics of the analytics ID.

Note: In this step, the valid range corresponding to the analytics ID isdetermined by each client NWDAF based on data participating in targetmodel training. For another client NWDAF and another server NWDAF, dataparticipating in training is unknown.

Step 912: The server NWDAF registers the supported analytics ID and thecorresponding valid range with the NRF network element.

In this embodiment of this application, the valid range corresponding tothe analytics ID in step 912 includes a valid range of the analytics IDon the client NWDAF.

The analytics ID supported by the server NWDAF is also registered withthe NRF network element. This is applicable to a scenario where theNWDAF is deployed in layers. It is assumed that a third-party AF networkelement or an OAM network element requests, from a network side NWDAF, adata analytics result corresponding to the analytics ID in a large area.In this case, the AF network element or the OAM network element firstqueries the server NWDAF from the NRF network element. Then the serverNWDAF may separately request a sub-area data analytics result fromanother client NWDAF, and then sends the sub-area data analytics resultto the AF network element or the OAM network element after integration.

In the embodiment shown in FIG. 9A and FIG. 9B, a federated learningbased training process is introduced to a 5G network, so that data doesnot need to be transmitted out of a local domain of each client NWDAFparticipating in federated learning based training. Each client NWDAFparticipating in federated learning based training performs sub-modeltraining based on obtained data, and then each client NWDAFparticipating in federated learning based training provides a sub-modelobtained in each round of training for a server NWDAF, so that theserver NWDAF finally obtains an updated model through aggregation basedon the sub-model, then, the target model is obtained, so that a modeltraining process is performed. In this method, data leakage can beavoided, and because data training is performed by the client NWDAF,distributed training process can also accelerate an entire modeltraining speed.

As shown in FIG. 11 , for a network slice a whose S-NSSAI is of a typeA, a server NWDAF may be deployed to provide a service for the networkslice a, and then at least one client NWDAF is deployed in differentareas served by the network slice a or on different slice instancescorresponding to the network slice a. As shown in FIG. 11 , the networkslice a serves an area 1, an area 2, and an area 3, and slice instances:a slice instance (network slice instance, NSI) 1, an NSI 2, and an NSI 3are deployed in the network slice a. A client NWDAF 1 is deployed in thearea 1, or a client NWDAF 1 serves the NSI 1. A client NWDAF 2 isdeployed in the area 2, or a client NWDAF 2 serves the NSI 2. The clientNWDAF 3 is deployed in the area 3, or a client NWDAF 3 serves the NSI 3.

In an NWDAF information registration process, the server NWDAF registersinformation such as a supported NWDAF type (for example, a server),supported federated learning capability information (a horizontalfederated learning type and algorithm information), and supportedanalytics ID=Service Experience data analytics with an NRF networkelement. The client NWDAF 1 to the client NWDAF 3 register informationsuch as an NWDAF type (for example, a client), federated learningcapability information (a horizontal federated learning type andalgorithm information), and analytics ID=aervice experience dataanalytics that are supported by the client NWDAF 1 to the client NWDAF 3with the NRF network element. Refer to the registration process in step801 to step 804. For example, the client NWDAF 1, the client NWDAF 2,and the client NWDAF 3 support Horizontal FL and are of the client type.

Then, OAM triggers a subscription request to the server NWDAF, where thesubscription request is used to subscribe quality of experience (QoE) ofa service or service experience (service experience, service meanopinion score, or service MOS) of the network slice a. Based ontriggering of the subscription request from the OAM, the server NWDAFqueries, based on a type, a range, federated learning capabilityinformation supported by the required client NWDAF, and analyticsID=Service Experience by using the NRF network element, a client NWDAFlist that can perform horizontal federated learning, and screens, fromthe client NWDAF list, a target client NWDAF (for example, the clientNWDAF 1, the client NWDAF 2, and the client NWDAF 3) whose Load is lowerthan a load threshold to participate in horizontal federated training.

In a federated learning preparation phase, the server NWDAF firstdetermines that a relationship model between service experience andnetwork data that is to be determined through linear regression. Aservice experience (Service MOS) model may be represented as follows:

h(x)=w ₀ x ₀ +w ₁ x ₁ +w ₂ x ₂ +w ₃ x ₃ +w ₄ x ₄ +w x ₅ + . . . +w _(D)x _(D)

where

h(x) indicates the service experience, namely, Service MOS, as shown inTable 2;

x_(i)(i=0,1,2, . . . , D) indicates the network data, as shown in Table3; and

D is a dimension of the network data, w_(i)(i=0, 1, 2, . . . , D) is aweight of each piece of network data that affects the serviceexperience, and D is a dimension of a weight.

TABLE 2 Service data from an AF network element Data Data sourceDescription Application identifier AF network element Used to identify aservice (application ID) IP filter information AF network element IPquintuple, used to indicate a service data flow of the service Locationof application AF network element/NEF One or more DNAIs, used toidentify network element an access point of the service. Serviceexperience AF network element Service experience of the service dataflow Timestamp AF network element Time of the service data flow

TABLE 3 Network data from a 5G NF Data Data source Description Timestamp5GC NF network Time when the following data is element collectedLocation AMF network Terminal location element DNN SMF network DNN of aPDU session to which a element QoS flow belongs S-NSSAI SMF networkS-NSSAI of the PDU session to element which the QoS flow belongsapplication ID SMF network Application identifier element correspondingto the QoS flow IP filter information SMF network IP quintuplecorresponding to a element service in the QoS flow QoS flow identifier(QFI) SMF network QoS flow identifier element QoS flow bit rate UPFnetwork Observed uplink or downlink bit element rate/bandwidth of theQoS flow QoS flow packet delay UPF network Uplink or downlink packetdelay element of an observed QoS flow Quantity of transmitted packets ofUPF network Quantity of transmitted packets of the QoS flow (packettransmission) element the observed QoS flow Quantity of times of packetUPF network Quantity of times of packet retransmission of the QoS flowelement retransmission of the observed (packet retransmission) QoS flowReference signal received power OAM network Terminal air interface(RSRP) element measurement quantity: RSRP Reference signal receivedquality OAM network Terminal air interface (RSRQ) element measurementquantity: RSRQ signal to interference plus noise ratio OAM network UEair interface measurement (SINR) element quantity: SINR

In a training phase:

(1) The server NWDAF first determines an initial Service MOS model basedon history, and then delivers the initial Service MOS model, a data type(which is also referred to as a feature), algorithm type linearregression, a maximum quantity of iterations that correspond to eachx_(i)(i=0, 1, 2, . . . , D), and the like to the client NWDAF 1 to theclient NWDAF 3 participating in training.

(2) Each of the client NWDAF 1 to the client NWDAF 3 calculates agradient of a respective loss function of the client NWDAF 1 to theclient NWDAF 3 for w_(i)(i=0, 1, 2, . . . , D), and the gradient may bereferred to as the sub-model or a client NWDAF training intermediateresult in this embodiment of this application. Then, the client NWDAF 1to the client NWDAF 3 report, to the server NWDAF, sub-models obtainedthrough training and quantities of samples (in other words, quantitiesof service flows in Table 2 and Table 3) participating in training.

(3) The server NWDAF may perform, by using a model aggregation module inthe server NWDAF, weighted average aggregation on sub-models reported byall target clients NWDAF participating in horizontal federated training,to obtain an updated model.

(4) The server NWDAF sends the updated model to each of the client NWDAF1 to the client NWDAF 3 that participate in the horizontal federationtraining. Then, the client NWDAF 1 to the client NWDAF 3 update localparameters based on the updated model. When any one of the client NWDAF1 to the client NWDAF 3 determines that a quantity of iterations reachesa maximum quantity of sub-iterations, the client NWDAF terminatestraining, and continues to send, to the server NWDAF, a sub-modelobtained when the maximum quantity of iteration times is reached.

(5) When determining that the termination condition of federatedtraining is met (for example, the foregoing (2) to (4)), the serverNWDAF obtains the target model based on the updated model, and then themodel management module in the server NWDAF allocates one or more of theidentifier of the target model, the version identifier of the targetmodel, and the analytics ID corresponding to the service QoE in thenetwork slice a to the target model.

(6) The server NWDAF sends the target model and one or more of thetarget model, the version identifier of the target model, and theAnalytics ID to each of the client NWDAF 1 to the client NWDAF 3.

In an inference phase:

A. It is assumed that, to optimize resource configuration of the networkslice a, the OAM subscribes to service QoE information of the networkslice a from the server NWDAF.

B. The server NWDAF requests the service QoE information in eachcorresponding sub-area or slice instance from each of the managed clientNWDAF 1 to the managed client NWDAF 3.

C. The client NWDAF 1 sends service QoE information of a sub-area 1 oran NSI 1 to the server NWDAF, the client NWDAF 2 sends service QoEinformation of a sub-area 2 or an NSI 2 to the server NWDAF, and theclient NWDAF 3 sends service QoE information of a sub-area 3 or an NSI 3to the server NWDAF. Then, the server NWDAF summarizes service QoEinformation of all sub-areas or slice instances to obtain the serviceQoE information of the network slice a, and sends the service QoEinformation to the OAM.

For example, the client NWDAF 1 obtains the service QoE information ofthe area 1 or the NSI 1 based on the target model and data correspondingto the area 1 or the NSI 1. The client NWDAF 2 obtains the service QoEinformation of the area 2 or the NSI 2 based on the target model anddata corresponding to the area 2 or the NSI 2. The client NWDAF 3obtains the service QoE information of the area 3 or the NSI 3 based onthe target model and data corresponding to the area 3 or the NSI 3.

D. The OAM determines, based on the service QoE information of thenetwork slice a, whether SLA of the network slice a is met. If the SLAof the network slice a is not met, the SLA of the network slice a may bemet by adjusting an air interface resource, a core network resource, ora transmission network configuration of the network slice a.

The foregoing mainly describes the solutions in embodiments of thisapplication from a perspective of interaction between network elements.It may be understood that, to implement the foregoing functions, thenetwork elements such as the first data analytics network element, theservice discovery network element, and the third data analytics networkelement include corresponding hardware structures and/or softwaremodules for performing the functions. A person skilled in the art shouldeasily be aware that, in combination with units and algorithm steps ofthe examples described in embodiments disclosed in this specification,this application may be implemented by hardware or a combination ofhardware and computer software. Whether a function is performed byhardware or hardware driven by computer software depends on particularapplications and design constraints of the technical solutions. A personskilled in the art may use different methods to implement the describedfunctions for each particular application, but it should not beconsidered that the implementation goes beyond the scope of thisapplication.

In embodiments of this application, functional unit division may beperformed based on the first data analytics network element, the servicediscovery network element, and the third data analytics network elementin the foregoing method examples. For example, each functional unit maybe obtained through division based on each corresponding function, ortwo or more functions may be integrated into one processing unit. Theintegrated unit may be implemented in a form of hardware, or may beimplemented in a form of a software functional unit. It should be notedthat, in embodiments of this application, division into the units is anexample, and is only a logical function division. In actualimplementation, another division manner may be used.

The foregoing describes the methods in embodiments of this applicationwith reference to FIG. 6 to FIG. 11 . The following describes acommunication apparatus that is provided in an embodiment of thisapplication and that performs the foregoing methods. A person skilled inthe art may understand that the method and the apparatus may be mutuallycombined and referenced. The communication apparatus provided in thisembodiment of this application may perform the steps performed by thefirst data analytics network element, the service discovery networkelement, and the third data analytics network element in the foregoingcommunication methods.

FIG. 12 shows a communication apparatus in the foregoing embodiments.The communication apparatus may include a communication unit 1202 and aprocessing unit 1201. The processing unit 1201 is configured to supportthe communication apparatus in performing an information processingaction. The communication unit 1202 is configured to support thecommunication apparatus in performing an information receiving orsending action.

In an example, the communication apparatus is a first data analyticsnetwork element, or a chip used in a first data analytics networkelement. In this case, the communication unit 1202 is configured tosupport the communication apparatus in performing a sending actionperformed by the first data analytics network element in step 601 inFIG. 6 in the foregoing embodiments. The communication unit 1202 isconfigured to support the communication apparatus in performing areceiving action performed by the first data analytics network elementin step 603 in FIG. 6 . The processing unit is further configured tosupport the communication apparatus in performing a processing actionperformed by the first data analytics network element in the foregoingembodiments.

In a possible embodiment, the communication unit 1202 is furtherconfigured to support the communication apparatus in performing sendingactions performed by the first data analytics network element in step701, step 712, and step 714 in the foregoing embodiments. The processingunit 1201 is further configured to support the communication apparatusin performing step 708, step 711, and step 713 in the foregoingembodiments.

In another example, the communication apparatus is a third dataanalytics network element, or a chip used in a third data analyticsnetwork element. In this case, the processing unit 1201 is configured tosupport the communication apparatus in performing a processing actionperformed by the third data analytics network element in step 709 in theforegoing embodiments. The communication unit 1202 is configured tosupport the communication apparatus in performing a sending actionperformed by the third data analytics network element in step 710 in theforegoing embodiments.

In a possible implementation, the communication unit 1202 is furtherconfigured to support the communication apparatus in performing areceiving action performed by the third data analytics network elementin step 712, a receiving action performed by a second data analyticsnetwork element in step 714, and a sending action performed by thesecond data analytics network element in step 703 in the foregoingembodiments.

In still another example, the communication apparatus is a servicediscovery network element, or a chip used in a service discovery networkelement. In this case, the communication unit 1202 is configured tosupport the communication apparatus in performing a receiving actionperformed by the service discovery network element in step 601 in FIG. 6in the foregoing embodiments. The processing unit 1201 is furtherconfigured to support the communication apparatus in performing aprocessing action performed by the service discovery network element instep 602 in the foregoing embodiments. The communication unit 1202 isconfigured to support the communication apparatus in performing asending action performed by the service discovery network element instep 603 in FIG. 6 .

In a possible embodiment, the communication unit 1202 is furtherconfigured to support the communication apparatus in performingreceiving actions performed by the service discovery network element instep 701 and step 703 in the foregoing embodiments. The processing unit1201 is configured to support the communication apparatus in performingprocessing actions performed by the service discovery network element instep 702 and step 704 in the foregoing embodiments.

FIG. 13 is a possible schematic diagram of a logical structure of acommunication apparatus in the foregoing embodiments. The communicationapparatus includes a processing module 1312 and a communication module1313. The processing module 1312 is configured to control and manage anaction of the communication apparatus. For example, the processingmodule 1312 is configured to perform an information/data processing stepperformed by the communication apparatus. The communication module 1313is configured to support the communication apparatus in performing aninformation/data sending or receiving step.

In a possible embodiment, the communication apparatus may furtherinclude a storage module 1311, configured to store program code and dataof the communication apparatus.

In an example, the communication apparatus is a first data analyticsnetwork element, or a chip used in a first data analytics networkelement. In this case, the communication module 1313 is configured tosupport the communication apparatus in performing a sending actionperformed by the first data analytics network element in step 601 inFIG. 6 in the foregoing embodiments. The communication module 1313 isconfigured to support the communication apparatus in performing areceiving action performed by the first data analytics network elementin step 603 in FIG. 6 . The processing module is further configured tosupport the communication apparatus in performing a processing actionperformed by the first data analytics network element in the foregoingembodiments.

In a possible embodiment, the communication module 1313 is furtherconfigured to support the communication apparatus in performing sendingactions performed by the first data analytics network element in step701, step 712, and step 714 in the foregoing embodiments. The processingmodule 1312 is further configured to support the communication apparatusin performing step 708, step 711, and step 713 in the foregoingembodiments.

In another example, the communication apparatus is a third dataanalytics network element, or a chip used in a third data analyticsnetwork element. In this case, the processing module 1312 is configuredto support the communication apparatus in performing a processing actionperformed by the third data analytics network element in step 709 in theforegoing embodiments. The communication module 1313 is configured tosupport the communication apparatus in performing a sending actionperformed by the third data analytics network element in step 710 in theforegoing embodiments.

In a possible implementation, the communication module 1313 is furtherconfigured to support the communication apparatus in performing areceiving action performed by the third data analytics network elementin step 712, a receiving action performed by a second data analyticsnetwork element in step 714, and a sending action performed by thesecond data analytics network element in step 703 in the foregoingembodiments.

In still another example, the communication apparatus is a servicediscovery network element, or a chip used in a service discovery networkelement. In this case, the communication module 1313 is configured tosupport the communication apparatus in performing a receiving actionperformed by the service discovery network element in step 601 in FIG. 6in the foregoing embodiments. The processing module 1312 is furtherconfigured to support the communication apparatus in performing aprocessing action performed by the service discovery network element instep 602 in the foregoing embodiments. The communication module 1313 isconfigured to support the communication apparatus in performing asending action performed by the service discovery network element instep 603 in FIG. 6 .

In a possible embodiment, the communication module 1313 is furtherconfigured to support the communication apparatus in performingreceiving actions performed by the service discovery network element instep 701 and step 703 in the foregoing embodiments. The processingmodule 1312 is configured to support the communication apparatus inperforming processing actions performed by the service discovery networkelement in step 702 and step 704 in the foregoing embodiments.

The processing module 1312 may be a processor or controller, forexample, the processing module may be a central processing unit, ageneral-purpose processor, a digital signal processor, anapplication-specific integrated circuit, a field programmable gate arrayor another programmable logic device, a transistor logic device, ahardware component, or any combination thereof. The processor mayimplement or execute various example logical blocks, modules, andcircuits described with reference to content disclosed in thisapplication. Alternatively, the processor may be a combination ofprocessors implementing a computing function, for example, a combinationof one or more microprocessors, or a combination of the digital signalprocessor and a microprocessor. The communication module 1313 may be atransceiver, a transceiver circuit, a communication interface, or thelike. The storage module 1311 may be a memory.

When the processing module 1312 is a processor 1401 or a processor 1405,the communication module 1313 is a communication interface 1403, and thestorage module 1311 is a memory 1402, the communication apparatus inthis application may be a communication device shown in FIG. 14 .

FIG. 14 is a schematic diagram of a hardware structure of acommunication device according to an embodiment of this application. Thecommunication device includes the processor 1401, a communication line1404, and at least one communication interface (in FIG. 14 , an examplein which the communication interface 1403 is included is only used fordescription).

In a possible implementation, the communication device may furtherinclude the memory 1402.

The processor 1401 may be a general-purpose central processing unit(CPU), a microprocessor, an application-specific integrated circuit(ASIC), or one or more integrated circuits for controlling programexecution of the solutions of this application.

The communication line 1404 may include a path for transferringinformation between the foregoing components.

The communication interface 1403 is applicable to any apparatus such asa transceiver, and is configured to communicate with another device or acommunication network such as Ethernet, a radio access network (RAN), ora wireless local area network (WLAN).

The memory 1402 may be a read-only memory (ROM) or another type ofstatic storage device that can store static information andinstructions, a random access memory (RAM) or another type of dynamicstorage device that can store information and instructions, or may be anelectrically erasable programmable read-only memory (EEPROM), a compactdisc read-only memory (CD-ROM) or another optical disc storage, anoptical disc storage (including a compressed optical disc, a laser disc,an optical disc, a digital versatile disc, a Blu-ray disc, or the like),a magnetic disk storage medium or another magnetic storage device, orany other medium that can be configured to carry or store expectedprogram code in a form of instructions or a data structure and that canbe accessed by a computer, but is not limited thereto. The memory mayexist independently, and is connected to the processor through thecommunication line 1404. The memory may alternatively be integrated withthe processor.

The memory 1402 is configured to store computer-executable instructionsfor performing the solutions in this application, and the processor 1401controls execution of the computer-executable instructions. Theprocessor 1401 is configured to execute the computer-executableinstructions stored in the memory 1402, to implement the communicationmethod provided in the following embodiments of this application.

In some embodiments, the computer-executable instructions may also bereferred to as application code. This is not specifically limited.

In some embodiments, the processor 1401 may include one or more CPUs,for example, a CPU 0 and a CPU 1 in FIG. 14 .

In some embodiments, the communication device may include a plurality ofprocessors, for example, the processor 1401 and the processor 1405 inFIG. 14 . Each of the processors may be a single-core (single-CPU)processor, or may be a multi-core (multi-CPU) processor. The processorherein may be one or more devices, circuits, and/or processing coresconfigured to process data (for example, computer program instructions).

In an example, the communication device is a first data analyticsnetwork element, or a chip used in a first data analytics networkelement. In this case, the communication interface 1403 is configured tosupport the communication device in performing a sending actionperformed by the first data analytics network element in step 601 inFIG. 6 in the foregoing embodiments. The communication interface 1403 isconfigured to support the communication device in performing a receivingaction performed by the first data analytics network element in step 603in FIG. 6 . The processing unit is further configured to support thecommunication device in performing a processing action performed by thefirst data analytics network element in the foregoing embodiments.

In a possible embodiment, the communication interface 1403 is furtherconfigured to support the communication device in performing sendingactions performed by the first data analytics network element in step701, step 712, and step 714 in the foregoing embodiments. The processor1401 and the processor 1405 are further configured to support thecommunication device in performing step 708, step 711, and step 713 inthe foregoing embodiments.

In another example, the communication device is a third data analyticsnetwork element, or a chip used in a third data analytics networkelement. In this case, the processor 1401 and the processor 1405 areconfigured to support the communication device in performing aprocessing action performed by the third data analytics network elementin step 709 in the foregoing embodiments. The communication interface1403 is configured to support the communication device in performing asending action performed by the third data analytics network element instep 710 in the foregoing embodiments.

In a possible implementation, the communication interface 1403 isfurther configured to support the communication device in performing areceiving action performed by the third data analytics network elementin step 712, a receiving action performed by a second data analyticsnetwork element in step 714, and a sending action performed by thesecond data analytics network element in step 703 in the foregoingembodiments.

In still another example, the communication device is a servicediscovery network element, or a chip used in a service discovery networkelement. In this case, the communication interface 1403 is configured tosupport the communication device in performing a receiving actionperformed by the service discovery network element in step 601 in FIG. 6in the foregoing embodiments. The processor 1401 and the processor 1405are further configured to support the communication device in performinga processing action performed by the service discovery network elementin step 602 in the foregoing embodiments. The communication interface1403 is configured to support the communication device in performing asending action performed by the service discovery network element instep 603 in FIG. 6 .

In a possible embodiment, the communication interface 1403 is furtherconfigured to support the communication device in performing receivingactions performed by the service discovery network element in step 701and step 703 in the foregoing embodiments. The processor 1401 and theprocessor 1405 are configured to support the communication device inperforming processing actions performed by the service discovery networkelement in step 702 and step 704 in the foregoing embodiments.

FIG. 15 is a schematic diagram of a structure of a chip 150 according toan embodiment of this application. The chip 150 includes one or more(including two) processors 1510 and a communication interface 1530.

In a possible implementation, the chip 150 further includes a memory1540. The memory 1540 may include a read-only memory and a random accessmemory, and provide operation instructions and data for the processor1510. A part of the memory 1540 may further include a non-volatilerandom access memory (NVRAM).

In some implementations, the memory 1540 stores the following elements:an executable module or a data structure, a subset thereof, or anextended set thereof.

In this embodiment of this application, the operation instructionsstored in the memory 1540 (where the operation instructions may bestored in an operating system) are invoked to perform a correspondingoperation.

In a possible implementation, structures of chips used by a first dataanalytics network element, a third data analytics network element, and aservice discovery network element are similar, and different apparatusesmay use different chips to implement respective functions.

The processor 1510 controls a processing operation of any one of thefirst data analytics network element, the third data analytics networkelement, and the service discovery network element. The processor 1510may also be referred to as a central processing unit (CPU).

The memory 1540 may include the read-only memory and the random accessmemory, and provide the instructions and the data for the processor1510. The part of the memory 1540 may further include the NVRAM. Forexample, in an application, the memory 1540, the communication interface1530, and the memory 1540 are coupled together through a bus system1520. The bus system 1520 may further include a power bus, a controlbus, a status signal bus, and the like in addition to a data bus.However, for clear description, various types of buses in FIG. 15 aremarked as the bus system 1520.

The methods disclosed in the foregoing embodiments of this applicationmay be applied to the processor 1510, or may be implemented by theprocessor 1510. The processor 1510 may be an integrated circuit chip,and has a signal processing capability. In an implementation process,the steps in the foregoing methods may be implemented by using ahardware integrated logical circuit in the processor 1510, or by usinginstructions in a form of software. The processor 1510 may be ageneral-purpose processor, a digital signal processor (DSP), an ASIC, afield-programmable gate array (FPGA) or another programmable logicdevice, a discrete gate or a transistor logic device, or a discretehardware component. It may implement or perform the methods, the steps,and logical block diagrams that are disclosed in embodiments of thisapplication. The general-purpose processor may be a microprocessor, orthe processor may be any conventional processor or the like. Steps ofthe methods disclosed with reference to embodiments of this applicationmay be directly executed and accomplished by using a hardware decodingprocessor, or may be executed and accomplished by using a combination ofhardware and software modules in the decoding processor. A softwaremodule may be located in a mature storage medium in the art, such as arandom access memory, a flash memory, a read-only memory, a programmableread-only memory, an electrically erasable programmable memory, or aregister. The storage medium is located in the memory 1540, and theprocessor 1510 reads information in the memory 1540 and completes thesteps in the foregoing methods in combination with hardware of theprocessor 1510.

In a possible implementation, the communication interface 1530 isconfigured to perform receiving and sending steps of the first dataanalytics network element, the third data analytics network element, andthe service discovery network element in embodiments shown in FIG. 6 andFIG. 7A and FIG. 7B. The processor 1510 is configured to performprocessing steps of the first data analytics network element, the thirddata analytics network element, and the service discovery networkelement in embodiments shown in FIG. 6 and FIG. 7A and FIG. 7B.

The communication unit may be a communication interface of theapparatus, and is configured to receive a signal from another apparatus.For example, when the apparatus is implemented as the chip, thecommunication unit is a communication interface used by the chip toreceive a signal from or send a signal to another chip or apparatus.

According to an aspect, a computer-readable storage medium is provided.The computer-readable storage medium stores instructions. When theinstructions are run, the functions of the first data analytics networkelement in FIG. 6 and FIG. 7A and FIG. 7B are implemented.

According to an aspect, a computer-readable storage medium is provided.The computer-readable storage medium stores instructions. When theinstructions are run, the functions of the third data analytics networkelement in FIG. 6 and FIG. 7A and FIG. 7B are implemented.

According to an aspect, a computer-readable storage medium is provided.The computer-readable storage medium stores instructions. When theinstructions are run, the functions of the first data analytics networkelement in FIG. 6 and FIG. 7A and FIG. 7B are implemented.

According to an aspect, a computer program product includinginstructions is provided. The computer program product includes theinstructions. When the instructions are run, the functions of the firstdata analytics network element in FIG. 6 and FIG. 7A and FIG. 7B areimplemented.

According to another aspect, a computer program product includinginstructions is provided. The computer program product includes theinstructions. When the instructions are run, the functions of the thirddata analytics network element in FIG. 6 and FIG. 7A and FIG. 7B areimplemented.

According to another aspect, a computer program product includinginstructions is provided. The computer program product includes theinstructions. When the instructions are run, the functions of theservice discovery network element in FIG. 6 and FIG. 7A and FIG. 7B areimplemented.

According to an aspect, a chip is provided. The chip is used in a firstdata analytics network element. The chip includes at least one processorand a communication interface. The communication interface is coupled tothe at least one processor. The processor is configured to runinstructions, to implement the functions of the first data analyticsnetwork element in FIG. 6 and FIG. 7A and FIG. 7B.

According to another aspect, a chip is provided. The chip is used athird data analytics network element. The chip includes at least oneprocessor and a communication interface. The communication interface iscoupled to the at least one processor. The processor is configured torun instructions, to implement the functions of the third data analyticsnetwork element in FIG. 6 and FIG. 7A and FIG. 7B.

According to another aspect, a chip is provided. The chip is used in aservice discovery network element. The chip includes at least oneprocessor and a communication interface. The communication interface iscoupled to the at least one processor. The processor is configured torun instructions, to implement the functions of the service discoverynetwork element in FIG. 6 and FIG. 7A and FIG. 7B.

An embodiment of this application provides a communication system. Thecommunication system includes a first data analytics network element anda service discovery network element. The first data analytics networkelement is configured to perform the function performed by the firstdata analytics network element in any one of FIG. 6 and FIG. 7A and FIG.7B, and the service discovery network element is configured to performthe steps performed by the service discovery network element in any oneof FIG. 6 and FIG. 7A and FIG. 7B.

In a possible implementation, the communication system may furtherinclude a third data analytics network element. The third data analyticsnetwork element is configured to perform the functions performed by thefirst data analytics network element and the third data analyticsnetwork element in FIG. 6 and FIG. 7A and FIG. 7B.

All or some of the foregoing embodiments may be implemented by usingsoftware, hardware, firmware, or any combination thereof. When thesoftware is used to implement the foregoing embodiments, all or some ofthe foregoing embodiments may be implemented in a form of a computerprogram product. The computer program product includes one or morecomputer programs or instructions. When the computer programs or theinstructions are loaded and executed on a computer, the procedures orthe functions according to embodiments of this application are all orpartially implemented. The computer may be a general-purpose computer, adedicated computer, a computer network, a network device, userequipment, or another programmable apparatus. The computer programs orthe instructions may be stored in a computer-readable storage medium, ormay be transmitted from a computer-readable storage medium to anothercomputer-readable storage medium. For example, the computer programs orthe instructions may be transmitted from a website, computer, server, ordata center to another website, computer, server, or data center in awired or wireless manner. The computer-readable storage medium may beany usable medium accessible by the computer, or may be a data storagedevice, such as a server or a data center, integrating one or moreusable media. The usable medium may be a magnetic medium, for example, afloppy disk, a hard disk, or a magnetic tape, may be an optical medium,for example, a digital video disc (DVD), or may be a semiconductormedium, for example, a solid-state drive (SSD).

Although this application is described with reference to embodiments, ina process of implementing this application that claims protection, aperson skilled in the art may understand and implement another variationof the disclosed embodiments by viewing the accompanying drawings,disclosed content, and the appended claims. In the claims, “comprising”does not exclude another component or another step, and “a” or “one”does not exclude a case of multiple. A single processor or another unitmay implement several functions enumerated in the claims. Some measuresare recorded in dependent claims that are different from each other, butthis does not mean that these measures cannot be combined to produce abetter effect.

Although this application is described with reference to specificfeatures and embodiments thereof, it is clear that various modificationsand combinations may be made to them without departing from the spiritand scope of this application. Correspondingly, the specification andaccompanying drawings are only example descriptions of this applicationdefined by the appended claims, and are considered as any of or allmodifications, variations, combinations or equivalents that cover thescope of this application. It is clear that a person skilled in the artcan make various modifications and variations to this applicationwithout departing from the spirit and scope of this application. Thisapplication is intended to cover these modifications and variations ofthis application provided that they fall within the scope of protectiondefined by the following claims and their equivalent technologies.

1. A communication method, comprising: sending, by a first dataanalytics network element, a first request to a service discoverynetwork element, wherein the first request requests information about asecond data analytics network element, the first request comprises oneor more of information about distributed learning or first indicationinformation, the information about distributed learning comprises a typeof distributed learning, and the first indication information indicatesa type of the second data analytics network element; and receiving, bythe first data analytics network element, information about the seconddata analytics network elements from the service discovery networkelement, wherein the second data analytics network element supports thetype of distributed learning.
 2. The communication method according toclaim 1, wherein the method further comprises: determining, by the firstdata analytics network element based on the information about the seconddata analytics network elements, information about a third dataanalytics network element that performs distributed learning.
 3. Thecommunication method according to claim 2, wherein a load of the thirddata analytics network element is lower than a preset load threshold, ora priority of the third data analytics network element is higher than apreset priority threshold.
 4. The communication method according toclaim 2, wherein the information about distributed learning furthercomprises algorithm information supported by distributed learning, andthe second data analytics network element or the third data analyticsnetwork element supports an algorithm corresponding to the algorithminformation supported by distributed learning.
 5. The communicationmethod according to claim 2, further comprising: receiving, by the firstdata analytics network element, a sub-model from the third dataanalytics network element, wherein the sub-model is obtained by thethird data analytics network element through training based on dataobtained by the third data analytics network element; determining, bythe first data analytics network element, an updated model based on thesub-model from the third data analytics network element; and sending, bythe first data analytics network element, the updated model to the thirddata analytics network element.
 6. The communication method according toclaim 5, further comprising: determining, by the first data analyticsnetwork element, a target model based on the updated model; and sending,by the first data analytics network element to the second data analyticsnetwork element, the target model and one or more of a model identifiercorresponding to the target model, a model version identifiercorresponding to the target model, or a data analytics identifiercorresponding to the target model.
 7. The communication method accordingto claim 5, further comprising: sending, by the first data analyticsnetwork element before receiving the sub-model from the third dataanalytics network element, a configuration parameter to the third dataanalytics network element, wherein the configuration parameter is aparameter used by the third data analytics network element to determinethe sub-model.
 8. The communication method according to claim 7, whereinthe configuration parameter comprises one or more of an initial model, atraining set selection criterion, a feature generation method, atraining termination condition, a maximum training time, or a maximumwaiting time.
 9. The communication method according to claim 1, whereinthe type of distributed learning comprises one of horizontal learning,vertical learning, or transfer learning, and the type of the second dataanalytics network element is one of a client, a local trainer, or apartial trainer.
 10. The communication method according to claim 1,further comprising: sending, by the first data analytics networkelement, a second request to the service discovery network element,wherein the second request requests to register information about thefirst data analytics network element, the information about the firstdata analytics network element comprises one or more of the informationabout distributed learning, a range of the first data analytics networkelement, or second indication information, and the second indicationinformation indicates a type of the first data analytics networkelement.
 11. The communication method according to claim 10, wherein thefirst request further comprises the range of the first data analyticsnetwork element, and a range of the second data analytics networkelement or a range of the third data analytics network element fallswithin the range of the first data analytics network element.
 12. Thecommunication method according to claim 11, wherein the range of thefirst data analytics network element comprises one or more of an areaserved by the first data analytics network element, a public land mobilenetwork (PLMN) identifier to which the first data analytics networkelement belongs, information about a network slice served by the firstdata analytics network element, a data network name (DNN) served by thefirst data analytics network element, or device vendor information ofthe first data analytics network element.
 13. The communication methodaccording to claim 10, wherein the type of the first data analyticsnetwork element comprises one of a server, a coordinator, a centralizedtrainer, or a global trainer.
 14. The communication method according toclaim 1, wherein the distributed learning is federated learning.
 15. Thecommunication method according to claim 1, wherein the second dataanalytics network element is a terminal.
 16. A communication apparatus,comprising: a processor; and a memory having instructions stored thereonthat, when executed by the processor, cause the apparatus to: send afirst request to a service discovery network element, wherein the firstrequest requests information about a data analytics network element, thefirst request comprises one or more of information about distributedlearning or first indication information, the information aboutdistributed learning comprises a type of distributed learning, and thefirst indication information indicates a type of the data analyticsnetwork element; and receive information about the data analyticsnetwork elements from the service discovery network element, wherein thedata analytics network element supports the type of distributedlearning.
 17. The communication apparatus according to claim 16, whereinthe apparatus is further caused to: determine, based on the informationabout the data analytics network elements, information about a differentdata analytics network element that performs distributed learning. 18.The communication apparatus according to claim 17, wherein the apparatusis further caused to: receive a sub-model from the different dataanalytics network element, wherein the sub-model is obtained by thedifferent data analytics network element through training based on dataobtained by the different data analytics network element; determine anupdated model based on the sub-model from the different data analyticsnetwork element; and send the updated model to the different dataanalytics network element.
 19. The communication apparatus according toclaim 18, wherein the apparatus is further caused to: determine a targetmodel based on the updated model; and send, to the data analyticsnetwork element, the target model and one or more of a model identifiercorresponding to the target model, a model version identifiercorresponding to the target model, or a data analytics identifiercorresponding to the target model.
 20. A communication system,comprising: a first data analytics network element; and a servicediscovery network element, wherein the first data analytics networkelement is configured to: send a first request to the service discoverynetwork element, wherein the first request requests information about asecond data analytics network element, the first request comprises oneor more of information about distributed learning or first indicationinformation, the information about distributed learning comprises a typeof distributed learning, and the first indication information indicatesa type of the second data analytics network element; and receiveinformation about the second data analytics network element from theservice discovery network element, the service discovery network elementis configured to: provide the information about the second dataanalytics network element for the first data analytics network elementin response to the first request from the first data analytics networkelement, and the second data analytics network element supports the typeof distributed learning indicated by the first indication informationincluded in the first request sent by the first data analytics networkelement.